Collecting network models and node information from a network

ABSTRACT

Systems, methods, and computer-readable media for collecting node information from a fabric and generating models based on the node information. In some examples, a system can obtain, from one or more controllers in a software-defined network (SDN), a logical model of the SDN, the logical model containing objects configured for the SDN from a hierarchical management information tree (MIT) associated with the SDN and representing configurations of the objects, the hierarchical MIT defining manageable objects and object properties for the SDN, the objects corresponding to the manageable objects. The system can obtain a topological model of a fabric associated with the SDN and, based on the topological model, poll nodes in the fabric for respective configurations at the nodes. Based on the respective configurations, the system can generate a node-specific representation of the logical model, the node-specific representation projecting the logical model on each node.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of, and priority to, U.S.Provisional Patent Application No. 62/520,670, filed on Jun. 16, 2017,entitled “NETWORK MODEL AND NODE INFORMATION COLLECTION FROM A NETWORK”,the contents of which are hereby expressly incorporated by reference inits entirety.

TECHNICAL FIELD

The present technology pertains to network configuration andtroubleshooting, and more specifically to network modeling and nodeinformation collection in a network fabric.

BACKGROUND

Computer networks are becoming increasingly complex, often involving lowlevel as well as high level configurations at various layers of thenetwork. For example, computer networks generally include numerousaccess policies, forwarding policies, routing policies, securitypolicies, quality-of-service (QoS) policies, etc., which together definethe overall behavior and operation of the network. Network operatorshave a wide array of configuration options for tailoring the network tothe needs of the users. While the different configuration optionsavailable provide network operators a great degree of flexibility andcontrol over the network, they also add to the complexity of thenetwork. In many cases, the configuration process can become highlycomplex. Not surprisingly, the network configuration process isincreasingly error prone. In addition, troubleshooting errors in ahighly complex network can be extremely difficult. The process ofidentifying the root cause of undesired behavior in the network can be adaunting task.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to describe the manner in which the above-recited and otheradvantages and features of the disclosure can be obtained, a moreparticular description of the principles briefly described above will berendered by reference to specific embodiments thereof which areillustrated in the appended drawings. Understanding that these drawingsdepict only exemplary embodiments of the disclosure and are nottherefore to be considered to be limiting of its scope, the principlesherein are described and explained with additional specificity anddetail through the use of the accompanying drawings in which:

FIGS. 1A and 1B illustrate example network environments;

FIG. 2A illustrates an example object model for a network;

FIG. 2B illustrates an example object model for a tenant object in theexample object model from FIG. 2A;

FIG. 2C illustrates an example association of various objects in theexample object model from FIG. 2A;

FIG. 2D illustrates a schematic diagram of example models forimplementing the example object model from FIG. 2A;

FIG. 2E illustrates an equivalency diagram for different models;

FIG. 3A illustrates an example network assurance appliance;

FIG. 3B illustrates an example system for network assurance;

FIG. 3C illustrates a schematic diagram of an example static policyanalyzer;

FIG. 4A illustrates an example method for network assurance;

FIG. 4B illustrates an example method for obtaining a topological modelof the network, collecting configurations from the nodes in thetopological model, and generating a node-specific model;

FIG. 4C illustrates an example method for obtaining a network-widelogical model of the network and generating a node-specific logicalmodel based on the network-wide logical model;

FIGS. 5A through 5C illustrate example configurations of contracts andobjects in a network;

FIG. 6 illustrates an example configuration of an application profileobject in a network;

FIG. 7 illustrates an example computing device in accordance withvarious embodiments; and

FIG. 8 illustrates an example network device in accordance with variousembodiments.

DETAILED DESCRIPTION

Various embodiments of the disclosure are discussed in detail below.While specific implementations are discussed, it should be understoodthat this is done for illustration purposes only. A person skilled inthe relevant art will recognize that other components and configurationsmay be used without parting from the spirit and scope of the disclosure.Thus, the following description and drawings are illustrative and arenot to be construed as limiting. Numerous specific details are describedto provide a thorough understanding of the disclosure. However, incertain instances, well-known or conventional details are not describedin order to avoid obscuring the description. References to one or anembodiment in the present disclosure can be references to the sameembodiment or any embodiment; and, such references mean at least one ofthe embodiments.

Reference to “one embodiment” or “an embodiment” means that a particularfeature, structure, or characteristic described in connection with theembodiment is included in at least one embodiment of the disclosure. Theappearances of the phrase “in one embodiment” in various places in thespecification are not necessarily all referring to the same embodiment,nor are separate or alternative embodiments mutually exclusive of otherembodiments. Moreover, various features are described which may beexhibited by some embodiments and not by others.

The terms used in this specification generally have their ordinarymeanings in the art, within the context of the disclosure, and in thespecific context where each term is used. Alternative language andsynonyms may be used for any one or more of the terms discussed herein,and no special significance should be placed upon whether or not a termis elaborated or discussed herein. In some cases, synonyms for certainterms are provided. A recital of one or more synonyms does not excludethe use of other synonyms. The use of examples anywhere in thisspecification including examples of any terms discussed herein isillustrative only, and is not intended to further limit the scope andmeaning of the disclosure or of any example term. Likewise, thedisclosure is not limited to various embodiments given in thisspecification.

Without intent to limit the scope of the disclosure, examples ofinstruments, apparatus, methods and their related results according tothe embodiments of the present disclosure are given below. Note thattitles or subtitles may be used in the examples for convenience of areader, which in no way should limit the scope of the disclosure. Unlessotherwise defined, technical and scientific terms used herein have themeaning as commonly understood by one of ordinary skill in the art towhich this disclosure pertains. In the case of conflict, the presentdocument, including definitions will control.

Additional features and advantages of the disclosure will be set forthin the description which follows, and in part will be obvious from thedescription, or can be learned by practice of the herein disclosedprinciples. The features and advantages of the disclosure can berealized and obtained by means of the instruments and combinationsparticularly pointed out in the appended claims. These and otherfeatures of the disclosure will become more fully apparent from thefollowing description and appended claims, or can be learned by thepractice of the principles set forth herein.

Overview

Software-defined networks (SDNs), such as application-centricinfrastructure (ACI) networks, can be managed and configured from one ormore centralized network elements, such as application policyinfrastructure controllers (APICs) in an ACI network or network managersin other SDN networks. A network operator can define variousconfigurations, objects, rules, etc., for the SDN network, which can beimplemented by the one or more centralized network elements. Theconfiguration information provided by the network operator can reflectthe network operator's intent for the SDN network, meaning, how thenetwork operator intends for the SDN network and its components tooperate. Such user intents can be programmatically encapsulated inlogical models stored at the centralized network elements. The logicalmodels can represent the user intents and reflect the configuration ofthe SDN network. For example, the logical models can represent theobject and policy universe (e.g., endpoints, tenants, endpoint groups,networks or contexts, application profiles, services, domains, policies,etc.) as defined for the particular SDN network by the user intentsand/or centralized network elements.

In many cases, various nodes and/or controllers in a network may containrespective information or representations of the network and networkstate. For example, different controllers may store different logicalmodels of the network and each node in the fabric of the network maycontain its own configuration model for the network. The approaches setforth herein can collect the information at the various controllers andnodes in the network to generate network-wide models as well asdevice-specific models corresponding to the network-wide models. Thesemodeling approaches can provide significant insight, foresight, andvisibility into the network.

Disclosed herein are systems, methods, and computer-readable media forcollecting node information from a network fabric and generating networkmodels based on the collected node information. In some examples, asystem can obtain, from one or more controllers in a software-definednetwork (SDN), a logical model of the SDN, the logical model containinga plurality of objects configured for the SDN from a hierarchicalmanagement information tree (MIT) associated with the SDN andrepresenting configurations of the plurality of objects, thehierarchical MIT defining manageable objects and object properties forthe SDN, the plurality of objects being associated with, orcorresponding to, the manageable objects. The system can obtain atopological model of a fabric associated with the SDN and, based on thetopological model, poll nodes in the fabric for respectiveconfigurations at the nodes. Based on the respective configurations, thesystem can generate a node-specific representation of the logical model,the node-specific representation of the logical model projecting thelogical model on each respective node.

Example Embodiments

The disclosed technology addresses the need in the art for accurate andefficient network modeling and analysis. The present technology involvessystem, methods, and computer-readable media for obtaining networkmodels and collecting node information from a network fabric. Thepresent technology will be described in the following disclosure asfollows. The discussion begins with an introductory discussion ofnetwork assurance and a description of example computing environments,as illustrated in FIGS. 1A and 1B. A discussion of network models fornetwork assurance, as shown in FIGS. 2A through 2E, and network modelingand assurance systems and methods, as shown in FIGS. 3A through 3C andFIGS. 4A through 4C, will then follow. The discussion continues with adescription of various configurations in a network model, includingcontracts, endpoint groups, tenants, bridge domains, contexts, andapplication profiles, as shown in FIGS. 5A through 5C and FIG. 6. Thediscussion concludes with a description of example computing and networkdevices, as illustrated in FIGS. 7 and 8, including example hardwarecomponents suitable for hosting software applications and performingcomputing operations.

The disclosure now turns to an introductory discussion of networkassurance.

Network assurance is the guarantee or determination that the network isbehaving as intended by the network operator and has been configuredproperly (e.g., the network is doing what it is intended to do). Intentcan encompass various network operations, such as bridging, routing,security, service chaining, endpoints, compliance, QoS (Quality ofService), audits, etc. Intent can be embodied in one or more policies,settings, configurations, etc., defined for the network and individualnetwork elements (e.g., switches, routers, applications, resources,etc.). However, often times, the configurations, policies, etc., definedby a network operator are incorrect or not accurately reflected in theactual behavior of the network. For example, a network operatorspecifies a configuration A for one or more types of traffic but laterfinds out that the network is actually applying configuration B to thattraffic or otherwise processing that traffic in a manner that isinconsistent with configuration A. This can be a result of manydifferent causes, such as hardware errors, software bugs, varyingpriorities, configuration conflicts, misconfiguration of one or moresettings, improper rule rendering by devices, unexpected errors orevents, software upgrades, configuration changes, failures, etc. Asanother example, a network operator implements configuration C but oneor more other configurations result in the network behaving in a mannerthat is inconsistent with the intent reflected by the implementation ofconfiguration C. For example, such a situation can result whenconfiguration C conflicts with other configurations in the network.

The approaches herein can provide network assurance by modeling variousaspects of the network and/or performing consistency checks as well asother network assurance checks. The network assurance approaches hereincan be implemented in various types of networks, including a privatenetwork, such as a local area network (LAN); an enterprise network; astandalone or traditional network, such as a data center network; anetwork including a physical or underlay layer and a logical or overlaylayer, such as a VXLAN or software-defined network (SDN) (e.g.,Application Centric Infrastructure (ACI) or VMware NSX networks); etc.

Network models can be constructed for a network and implemented fornetwork assurance. A network model can provide a representation of oneor more aspects of a network, including, without limitation thenetwork's policies, configurations, requirements, security, routing,topology, applications, hardware, filters, contracts, access controllists, infrastructure, etc. As will be further explained below,different types of models can be generated for a network.

Such models can be implemented to ensure that the behavior of thenetwork will be consistent (or is consistent) with the intended behaviorreflected through specific configurations (e.g., policies, settings,definitions, etc.) implemented by the network operator. Unliketraditional network monitoring, which involves sending and analyzingdata packets and observing network behavior, network assurance can beperformed through modeling without necessarily ingesting packet data ormonitoring traffic or network behavior. This can result in foresight,insight, and hindsight: problems can be prevented before they occur,identified when they occur, and fixed immediately after they occur.

Thus, network assurance can involve modeling properties of the networkto deterministically predict the behavior of the network. The networkcan be determined to be healthy if the model(s) indicate proper behavior(e.g., no inconsistencies, conflicts, errors, etc.). The network can bedetermined to be functional, but not fully healthy, if the modelingindicates proper behavior but some inconsistencies. The network can bedetermined to be non-functional and not healthy if the modelingindicates improper behavior and errors. If inconsistencies or errors aredetected by the modeling, a detailed analysis of the correspondingmodel(s) can allow one or more underlying or root problems to beidentified with great accuracy.

The modeling can consume numerous types of smart events which model alarge amount of behavioral aspects of the network. Smart events canimpact various aspects of the network, such as underlay services,overlay services, tenant connectivity, tenant security, tenant end point(EP) mobility, tenant policy, tenant routing, resources, etc.

Having described various aspects of network assurance, the disclosurenow turns to a discussion of example network environments for networkassurance.

FIG. 1A illustrates a diagram of an example Network Environment 100,such as a data center. The Network Environment 100 can include a Fabric120 which can represent the physical layer or infrastructure (e.g.,underlay) of the Network Environment 100. Fabric 120 can include Spines102 (e.g., spine routers or switches) and Leafs 104 (e.g., leaf routersor switches) which can be interconnected for routing or switchingtraffic in the Fabric 120. Spines 102 can interconnect Leafs 104 in theFabric 120, and Leafs 104 can connect the Fabric 120 to an overlay orlogical portion of the Network Environment 100, which can includeapplication services, servers, virtual machines, containers, endpoints,etc. Thus, network connectivity in the Fabric 120 can flow from Spines102 to Leafs 104, and vice versa. The interconnections between Leafs 104and Spines 102 can be redundant (e.g., multiple interconnections) toavoid a failure in routing. In some embodiments, Leafs 104 and Spines102 can be fully connected, such that any given Leaf is connected toeach of the Spines 102, and any given Spine is connected to each of theLeafs 104. Leafs 104 can be, for example, top-of-rack (“ToR”) switches,aggregation switches, gateways, ingress and/or egress switches, provideredge devices, and/or any other type of routing or switching device.

Leafs 104 can be responsible for routing and/or bridging tenant orcustomer packets and applying network policies or rules. Networkpolicies and rules can be driven by one or more Controllers 116, and/orimplemented or enforced by one or more devices, such as Leafs 104. Leafs104 can connect other elements to the Fabric 120. For example, Leafs 104can connect Servers 106, Hypervisors 108, Virtual Machines (VMs) 110,Applications 112, Network Device 114, etc., with Fabric 120. Suchelements can reside in one or more logical or virtual layers ornetworks, such as an overlay network. In some cases, Leafs 104 canencapsulate and decapsulate packets to and from such elements (e.g.,Servers 106) in order to enable communications throughout NetworkEnvironment 100 and Fabric 120. Leafs 104 can also provide any otherdevices, services, tenants, or workloads with access to Fabric 120. Insome cases, Servers 106 connected to Leafs 104 can similarly encapsulateand decapsulate packets to and from Leafs 104. For example, Servers 106can include one or more virtual switches or routers or tunnel endpointsfor tunneling packets between an overlay or logical layer hosted by, orconnected to, Servers 106 and an underlay layer represented by Fabric120 and accessed via Leafs 104.

Applications 112 can include software applications, services,containers, appliances, functions, service chains, etc. For example,Applications 112 can include a firewall, a database, a CDN server, anIDS/IPS, a deep packet inspection service, a message router, a virtualswitch, etc. An application from Applications 112 can be distributed,chained, or hosted by multiple endpoints (e.g., Servers 106, VMs 110,etc.), or may run or execute entirely from a single endpoint.

VMs 110 can be virtual machines hosted by Hypervisors 108 or virtualmachine managers running on Servers 106. VMs 110 can include workloadsrunning on a guest operating system on a respective server. Hypervisors108 can provide a layer of software, firmware, and/or hardware thatcreates, manages, and/or runs the VMs 110. Hypervisors 108 can allow VMs110 to share hardware resources on Servers 106, and the hardwareresources on Servers 106 to appear as multiple, separate hardwareplatforms. Moreover, Hypervisors 108 on Servers 106 can host one or moreVMs 110.

In some cases, VMs 110 and/or Hypervisors 108 can be migrated to otherServers 106. Servers 106 can similarly be migrated to other locations inNetwork Environment 100. For example, a server connected to a specificleaf can be changed to connect to a different or additional leaf. Suchconfiguration or deployment changes can involve modifications tosettings, configurations and policies that are applied to the resourcesbeing migrated as well as other network components.

In some cases, one or more Servers 106, Hypervisors 108, and/or VMs 110can represent or reside in a tenant or customer space. Tenant space caninclude workloads, services, applications, devices, networks, and/orresources that are associated with one or more clients or subscribers.Accordingly, traffic in Network Environment 100 can be routed based onspecific tenant policies, spaces, agreements, configurations, etc.Moreover, addressing can vary between one or more tenants. In someconfigurations, tenant spaces can be divided into logical segmentsand/or networks and separated from logical segments and/or networksassociated with other tenants. Addressing, policy, security andconfiguration information between tenants can be managed by Controllers116, Servers 106, Leafs 104, etc.

Configurations in Network Environment 100 can be implemented at alogical level, a hardware level (e.g., physical), and/or both. Forexample, configurations can be implemented at a logical and/or hardwarelevel based on endpoint or resource attributes, such as endpoint typesand/or application groups or profiles, through a software-definednetwork (SDN) framework (e.g., Application-Centric Infrastructure (ACI)or VMWARE NSX). To illustrate, one or more administrators can defineconfigurations at a logical level (e.g., application or software level)through Controllers 116, which can implement or propagate suchconfigurations through Network Environment 100. In some examples,Controllers 116 can be Application Policy Infrastructure Controllers(APICs) in an ACI framework. In other examples, Controllers 116 can beone or more management components for associated with other SDNsolutions, such as NSX Managers.

Such configurations can define rules, policies, priorities, protocols,attributes, objects, etc., for routing and/or classifying traffic inNetwork Environment 100. For example, such configurations can defineattributes and objects for classifying and processing traffic based onEndpoint Groups (EPGs), Security Groups (SGs), VM types, bridge domains(BDs), virtual routing and forwarding instances (VRFs), tenants,priorities, firewall rules, etc. Other example network objects andconfigurations are further described below. Traffic policies and rulescan be enforced based on tags, attributes, or other characteristics ofthe traffic, such as protocols associated with the traffic, EPGsassociated with the traffic, SGs associated with the traffic, networkaddress information associated with the traffic, etc. Such policies andrules can be enforced by one or more elements in Network Environment100, such as Leafs 104, Servers 106, Hypervisors 108, Controllers 116,etc. As previously explained, Network Environment 100 can be configuredaccording to one or more particular software-defined network (SDN)solutions, such as CISCO ACI or VMWARE NSX. These example SDN solutionsare briefly described below.

ACI can provide an application-centric or policy-based solution throughscalable distributed enforcement. ACI supports integration of physicaland virtual environments under a declarative configuration model fornetworks, servers, services, security, requirements, etc. For example,the ACI framework implements EPGs, which can include a collection ofendpoints or applications that share common configuration requirements,such as security, QoS, services, etc. Endpoints can be virtual/logicalor physical devices, such as VMs, containers, hosts, or physical serversthat are connected to Network Environment 100. Endpoints can have one ormore attributes such as a VM name, guest OS name, a security tag,application profile, etc. Application configurations can be appliedbetween EPGs, instead of endpoints directly, in the form of contracts.Leafs 104 can classify incoming traffic into different EPGs. Theclassification can be based on, for example, a network segmentidentifier such as a VLAN ID, VXLAN Network Identifier (VNID), NVGREVirtual Subnet Identifier (VSID), MAC address, IP address, etc.

In some cases, classification in the ACI infrastructure can beimplemented by Application Virtual Switches (AVS), which can run on ahost, such as a server or switch. For example, an AVS can classifytraffic based on specified attributes, and tag packets of differentattribute EPGs with different identifiers, such as network segmentidentifiers (e.g., VLAN ID). Finally, Leafs 104 can tie packets withtheir attribute EPGs based on their identifiers and enforce policies,which can be implemented and/or managed by one or more Controllers 116.Leaf 104 can classify to which EPG the traffic from a host belongs andenforce policies accordingly.

Another example SDN solution is based on VMWARE NSX. With VMWARE NSX,hosts can run a distributed firewall (DFW) which can classify andprocess traffic. Consider a case where three types of VMs, namely,application, database and web VMs, are put into a single layer-2 networksegment. Traffic protection can be provided within the network segmentbased on the VM type. For example, HTTP traffic can be allowed among webVMs, and disallowed between a web VM and an application or database VM.To classify traffic and implement policies, VMWARE NSX can implementsecurity groups, which can be used to group the specific VMs (e.g., webVMs, application VMs, database VMs). DFW rules can be configured toimplement policies for the specific security groups. To illustrate, inthe context of the previous example, DFW rules can be configured toblock HTTP traffic between web, application, and database securitygroups.

Returning now to FIG. 1A, Network Environment 100 can deploy differenthosts via Leafs 104, Servers 106, Hypervisors 108, VMs 110, Applications112, and Controllers 116, such as VMWARE ESXi hosts, WINDOWS HYPER-Vhosts, bare metal physical hosts, etc.

Network Environment 100 may interoperate with a variety of Hypervisors108, Servers 106 (e.g., physical and/or virtual servers), SDNorchestration platforms, etc. Network Environment 100 may implement adeclarative model to allow its integration with application design andholistic network policy.

Controllers 116 can provide centralized access to fabric information,application configuration, resource configuration, application-levelconfiguration modeling for a software-defined network (SDN)infrastructure, integration with management systems or servers, etc.Controllers 116 can form a control plane that interfaces with anapplication plane via northbound APIs and a data plane via southboundAPIs.

As previously noted, Controllers 116 can define and manageapplication-level model(s) for configurations in Network Environment100. In some cases, application or device configurations can also bemanaged and/or defined by other components in the network. For example,a hypervisor or virtual appliance, such as a VM or container, can run aserver or management tool to manage software and services in NetworkEnvironment 100, including configurations and settings for virtualappliances.

As illustrated above, Network Environment 100 can include one or moredifferent types of SDN solutions, hosts, etc. For the sake of clarityand explanation purposes, various examples in the disclosure will bedescribed with reference to an ACI framework, and Controllers 116 may beinterchangeably referenced as controllers, APICs, or APIC controllers.However, it should be noted that the technologies and concepts hereinare not limited to ACI solutions and may be implemented in otherarchitectures and scenarios, including other SDN solutions as well asother types of networks which may not deploy an SDN solution.

Further, as referenced herein, the term “hosts” can refer to Servers 106(e.g., physical or logical), Hypervisors 108, VMs 110, containers (e.g.,Applications 112), etc., and can run or include any type of server orapplication solution. Non-limiting examples of “hosts” can includevirtual switches or routers, such as distributed virtual switches (DVS),application virtual switches (AVS), vector packet processing (VPP)switches; VCENTER and NSX MANAGERS; bare metal physical hosts; HYPER-Vhosts; VMs; DOCKER Containers; etc.

FIG. 1B illustrates another example of Network Environment 100. In thisexample, Network Environment 100 includes Endpoints 122 connected toLeafs 104 in Fabric 120. Endpoints 122 can be physical and/or logical orvirtual entities, such as servers, clients, VMs, hypervisors, softwarecontainers, applications, resources, network devices, workloads, etc.For example, an Endpoint 122 can be an object that represents a physicaldevice (e.g., server, client, switch, etc.), an application (e.g., webapplication, database application, etc.), a logical or virtual resource(e.g., a virtual switch, a virtual service appliance, a virtualizednetwork function (VNF), a VM, a service chain, etc.), a containerrunning a software resource (e.g., an application, an appliance, a VNF,a service chain, etc.), storage, a workload or workload engine, etc.Endpoints 122 can have an address (e.g., an identity), a location (e.g.,host, network segment, virtual routing and forwarding (VRF) instance,domain, etc.), one or more attributes (e.g., name, type, version, patchlevel, OS name, OS type, etc.), a tag (e.g., security tag), a profile,etc.

Endpoints 122 can be associated with respective Logical Groups 118.Logical Groups 118 can be logical entities containing endpoints(physical and/or logical or virtual) grouped together according to oneor more attributes, such as endpoint type (e.g., VM type, workload type,application type, etc.), one or more requirements (e.g., policyrequirements, security requirements, QoS requirements, customerrequirements, resource requirements, etc.), a resource name (e.g., VMname, application name, etc.), a profile, platform or operating system(OS) characteristics (e.g., OS type or name including guest and/or hostOS, etc.), an associated network or tenant, one or more policies, a tag,etc. For example, a logical group can be an object representing acollection of endpoints grouped together. To illustrate, Logical Group 1can contain client endpoints, Logical Group 2 can contain web serverendpoints, Logical Group 3 can contain application server endpoints,Logical Group N can contain database server endpoints, etc. In someexamples, Logical Groups 118 are EPGs in an ACI environment and/or otherlogical groups (e.g., SGs) in another SDN environment.

Traffic to and/or from Endpoints 122 can be classified, processed,managed, etc., based Logical Groups 118. For example, Logical Groups 118can be used to classify traffic to or from Endpoints 122, apply policiesto traffic to or from Endpoints 122, define relationships betweenEndpoints 122, define roles of Endpoints 122 (e.g., whether an endpointconsumes or provides a service, etc.), apply rules to traffic to or fromEndpoints 122, apply filters or access control lists (ACLs) to trafficto or from Endpoints 122, define communication paths for traffic to orfrom Endpoints 122, enforce requirements associated with Endpoints 122,implement security and other configurations associated with Endpoints122, etc.

In an ACI environment, Logical Groups 118 can be EPGs used to definecontracts in the ACI. Contracts can include rules specifying what andhow communications between EPGs take place. For example, a contract candefine what provides a service, what consumes a service, and what policyobjects are related to that consumption relationship. A contract caninclude a policy that defines the communication path and all relatedelements of a communication or relationship between endpoints or EPGs.For example, a Web EPG can provide a service that a Client EPG consumes,and that consumption can be subject to a filter (ACL) and a servicegraph that includes one or more services, such as firewall inspectionservices and server load balancing.

FIG. 2A illustrates a diagram of an example Management Information Model200 for an SDN network, such as Network Environment 100. The followingdiscussion of Management Information Model 200 references various termswhich shall also be used throughout the disclosure. Accordingly, forclarity, the disclosure shall first provide below a list of terminology,which will be followed by a more detailed discussion of ManagementInformation Model 200.

As used herein, an “Alias” can refer to a changeable name for a givenobject. Thus, even if the name of an object, once created, cannot bechanged, the Alias can be a field that can be changed.

As used herein, the term “Aliasing” can refer to a rule (e.g.,contracts, policies, configurations, etc.) that overlaps one or moreother rules. For example, Contract 1 defined in a logical model of anetwork can be said to be aliasing Contract 2 defined in the logicalmodel of the network if Contract 1 overlaps Contract 1. In this example,by aliasing Contract 2, Contract 1 may render Contract 2 redundant orinoperable. For example, if Contract 1 has a higher priority thanContract 2, such aliasing can render Contract 2 redundant based onContract 1's overlapping and higher priority characteristics.

As used herein, the term “APIC” can refer to one or more controllers(e.g., Controllers 116) in an ACI framework. The APIC can provide aunified point of automation and management, policy programming,application deployment, health monitoring for an ACI multitenant fabric.The APIC can be implemented as a single controller, a distributedcontroller, or a replicated, synchronized, and/or clustered controller.

As used herein, the term “BDD” can refer to a binary decision tree. Abinary decision tree can be a data structure representing functions,such as Boolean functions.

As used herein, the term “BD” can refer to a bridge domain. A bridgedomain can be a set of logical ports that share the same flooding orbroadcast characteristics. Like a virtual LAN (VLAN), bridge domains canspan multiple devices. A bridge domain can be a L2 (Layer 2) construct.

As used herein, a “Consumer” can refer to an endpoint, resource, and/orEPG that consumes a service.

As used herein, a “Context” can refer to an L3 (Layer 3) address domainthat allows multiple instances of a routing table to exist and worksimultaneously. This increases functionality by allowing network pathsto be segmented without using multiple devices. Non-limiting examples ofa context or L3 address domain can include a Virtual Routing andForwarding (VRF) instance, a private network, and so forth.

As used herein, the term “Contract” can refer to rules or configurationsthat specify what and how communications in a network are conducted(e.g., allowed, denied, filtered, processed, etc.). In an ACI network,contracts can specify how communications between endpoints and/or EPGstake place. In some examples, a contract can provide rules andconfigurations akin to an Access Control List (ACL).

As used herein, the term “Distinguished Name” (DN) can refer to a uniquename that describes an object, such as an MO, and locates its place inManagement Information Model 200. In some cases, the DN can be (orequate to) a Fully Qualified Domain Name (FQDN).

As used herein, the term “Endpoint Group” (EPG) can refer to a logicalentity or object associated with a collection or group of endoints aspreviously described with reference to FIG. 1B.

As used herein, the term “Filter” can refer to a parameter orconfiguration for allowing communications. For example, in a whitelistmodel where all communications are blocked by default, a communicationmust be given explicit permission to prevent such communication frombeing blocked. A filter can define permission(s) for one or morecommunications or packets. A filter can thus function similar to an ACLor Firewall rule. In some examples, a filter can be implemented in apacket (e.g., TCP/IP) header field, such as L3 protocol type, L4 (Layer4) ports, and so on, which is used to allow inbound or outboundcommunications between endpoints or EPGs, for example.

As used herein, the term “L2 Out” can refer to a bridged connection. Abridged connection can connect two or more segments of the same networkso that they can communicate. In an ACI framework, an L2 out can be abridged (Layer 2) connection between an ACI fabric (e.g., Fabric 120)and an outside Layer 2 network, such as a switch.

As used herein, the term “L3 Out” can refer to a routed connection. Arouted Layer 3 connection uses a set of protocols that determine thepath that data follows in order to travel across networks from itssource to its destination. Routed connections can perform forwarding(e.g., IP forwarding) according to a protocol selected, such as BGP(border gateway protocol), OSPF (Open Shortest Path First), EIGRP(Enhanced Interior Gateway Routing Protocol), etc.

As used herein, the term “Managed Object” (MO) can refer to an abstractrepresentation of objects that are managed in a network (e.g., NetworkEnvironment 100). The objects can be concrete objects (e.g., a switch,server, adapter, etc.), or logical objects (e.g., an applicationprofile, an EPG, a fault, etc.). The MOs can be network resources orelements that are managed in the network. For example, in an ACIenvironment, an MO can include an abstraction of an ACI fabric (e.g.,Fabric 120) resource.

As used herein, the term “Management Information Tree” (MIT) can referto a hierarchical management information tree containing the MOs of asystem. For example, in ACI, the MIT contains the MOs of the ACI fabric(e.g., Fabric 120). The MIT can also be referred to as a ManagementInformation Model (MIM), such as Management Information Model 200.

As used herein, the term “Policy” can refer to one or morespecifications for controlling some aspect of system or networkbehavior. For example, a policy can include a named entity that containsspecifications for controlling some aspect of system behavior. Toillustrate, a Layer 3 Outside Network Policy can contain the BGPprotocol to enable BGP routing functions when connecting Fabric 120 toan outside Layer 3 network.

As used herein, the term “Profile” can refer to the configurationdetails associated with a policy. For example, a profile can include anamed entity that contains the configuration details for implementingone or more instances of a policy. To illustrate, a switch node profilefor a routing policy can contain the switch-specific configurationdetails to implement the BGP routing protocol.

As used herein, the term “Provider” refers to an object or entityproviding a service. For example, a provider can be an EPG that providesa service.

As used herein, the term “Subject” refers to one or more parameters in acontract for defining communications. For example, in ACI, subjects in acontract can specify what information can be communicated and how.Subjects can function similar to ACLs.

As used herein, the term “Tenant” refers to a unit of isolation in anetwork. For example, a tenant can be a secure and exclusive virtualcomputing environment. In ACI, a tenant can be a unit of isolation froma policy perspective, but does not necessarily represent a privatenetwork. Indeed, ACI tenants can contain multiple private networks(e.g., VRFs). Tenants can represent a customer in a service providersetting, an organization or domain in an enterprise setting, or just agrouping of policies.

As used herein, the term “VRF” refers to a virtual routing andforwarding instance. The VRF can define a Layer 3 address domain thatallows multiple instances of a routing table to exist and worksimultaneously. This increases functionality by allowing network pathsto be segmented without using multiple devices. Also known as a contextor private network.

Having described various terms used herein, the disclosure now returnsto a discussion of Management Information Model (MIM) 200 in FIG. 2A. Aspreviously noted, MIM 200 can be a hierarchical management informationtree or MIT. Moreover, MIM 200 can be managed and processed byControllers 116, such as APICs in an ACI. Controllers 116 can enable thecontrol of managed resources by presenting their manageablecharacteristics as object properties that can be inherited according tothe location of the object within the hierarchical structure of themodel.

The hierarchical structure of MIM 200 starts with Policy Universe 202 atthe top (Root) and contains parent and child nodes 116, 204, 206, 208,210, 212. Nodes 116, 202, 204, 206, 208, 210, 212 in the tree representthe managed objects (MOs) or groups of objects. Each object in thefabric (e.g., Fabric 120) has a unique distinguished name (DN) thatdescribes the object and locates its place in the tree. The Nodes 116,202, 204, 206, 208, 210, 212 can include the various MOs, as describedbelow, which contain policies that govern the operation of the system.

Controllers 116

Controllers 116 (e.g., APIC controllers) can provide management, policyprogramming, application deployment, and health monitoring for Fabric120.

Node 204

Node 204 includes a tenant container for policies that enable anadministrator to exercise domain-based access control. Non-limitingexamples of tenants can include:

User tenants defined by the administrator according to the needs ofusers. They contain policies that govern the operation of resources suchas applications, databases, web servers, network-attached storage,virtual machines, and so on.

The common tenant is provided by the system but can be configured by theadministrator. It contains policies that govern the operation ofresources accessible to all tenants, such as firewalls, load balancers,Layer 4 to Layer 7 services, intrusion detection appliances, and so on.

The infrastructure tenant is provided by the system but can beconfigured by the administrator. It contains policies that govern theoperation of infrastructure resources such as the fabric overlay (e.g.,VXLAN). It also enables a fabric provider to selectively deployresources to one or more user tenants. Infrastructure tenant polices canbe configurable by the administrator.

The management tenant is provided by the system but can be configured bythe administrator. It contains policies that govern the operation offabric management functions used for in-band and out-of-bandconfiguration of fabric nodes. The management tenant contains a privateout-of-bound address space for the Controller/Fabric internalcommunications that is outside the fabric data path that provides accessthrough the management port of the switches. The management tenantenables discovery and automation of communications with virtual machinecontrollers.

Node 206

Node 206 can contain access policies that govern the operation of switchaccess ports that provide connectivity to resources such as storage,compute, Layer 2 and Layer 3 (bridged and routed) connectivity, virtualmachine hypervisors, Layer 4 to Layer 7 devices, and so on. If a tenantrequires interface configurations other than those provided in thedefault link, Cisco Discovery Protocol (CDP), Link Layer DiscoveryProtocol (LLDP), Link Aggregation Control Protocol (LACP), or SpanningTree Protocol (STP), an administrator can configure access policies toenable such configurations on the access ports of Leafs 104.

Node 206 can contain fabric policies that govern the operation of theswitch fabric ports, including such functions as Network Time Protocol(NTP) server synchronization, Intermediate System-to-Intermediate SystemProtocol (IS-IS), Border Gateway Protocol (BGP) route reflectors, DomainName System (DNS) and so on. The fabric MO contains objects such aspower supplies, fans, chassis, and so on.

Node 208

Node 208 can contain VM domains that group VM controllers with similarnetworking policy requirements. VM controllers can share virtual space(e.g., VLAN or VXLAN space) and application EPGs. Controllers 116communicate with the VM controller to publish network configurationssuch as port groups that are then applied to the virtual workloads.

Node 210

Node 210 can contain Layer 4 to Layer 7 service integration life cycleautomation framework that enables the system to dynamically respond whena service comes online or goes offline. Policies can provide servicedevice package and inventory management functions.

Node 212

Node 212 can contain access, authentication, and accounting (AAA)policies that govern user privileges, roles, and security domains ofFabric 120.

The hierarchical policy model can fit well with an API, such as a RESTAPI interface. When invoked, the API can read from or write to objectsin the MIT. URLs can map directly into distinguished names that identifyobjects in the MIT. Data in the MIT can be described as a self-containedstructured tree text document encoded in XML or JSON, for example.

FIG. 2B illustrates an example object model 220 for a tenant portion ofMIM 200. As previously noted, a tenant is a logical container forapplication policies that enable an administrator to exercisedomain-based access control. A tenant thus represents a unit ofisolation from a policy perspective, but it does not necessarilyrepresent a private network. Tenants can represent a customer in aservice provider setting, an organization or domain in an enterprisesetting, or just a convenient grouping of policies. Moreover, tenantscan be isolated from one another or can share resources.

Tenant portion 204A of MIM 200 can include various entities, and theentities in Tenant Portion 204A can inherit policies from parententities. Non-limiting examples of entities in Tenant Portion 204A caninclude Filters 240, Contracts 236, Outside Networks 222, Bridge Domains230, VRF Instances 234, and Application Profiles 224.

Bridge Domains 230 can include Subnets 232. Contracts 236 can includeSubjects 238. Application Profiles 224 can contain one or more EPGs 226.Some applications can contain multiple components. For example, ane-commerce application could require a web server, a database server,data located in a storage area network, and access to outside resourcesthat enable financial transactions. Application Profile 224 contains asmany (or as few) EPGs as necessary that are logically related toproviding the capabilities of an application.

EPG 226 can be organized in various ways, such as based on theapplication they provide, the function they provide (such asinfrastructure), where they are in the structure of the data center(such as DMZ), or whatever organizing principle that a fabric or tenantadministrator chooses to use.

EPGs in the fabric can contain various types of EPGs, such asapplication EPGs, Layer 2 external outside network instance EPGs, Layer3 external outside network instance EPGs, management EPGs forout-of-band or in-band access, etc. EPGs 226 can also contain Attributes228, such as encapsulation-based EPGs, IP-based EPGs, or MAC-based EPGs.

As previously mentioned, EPGs can contain endpoints (e.g., EPs 122) thathave common characteristics or attributes, such as common policyrequirements (e.g., security, virtual machine mobility (VMM), QoS, orLayer 4 to Layer 7 services). Rather than configure and manage endpointsindividually, they can be placed in an EPG and managed as a group.

Policies apply to EPGs, including the endpoints they contain. An EPG canbe statically configured by an administrator in Controllers 116, ordynamically configured by an automated system such as VCENTER orOPENSTACK.

To activate tenant policies in Tenant Portion 204A, fabric accesspolicies should be configured and associated with tenant policies.Access policies enable an administrator to configure other networkconfigurations, such as port channels and virtual port channels,protocols such as LLDP, CDP, or LACP, and features such as monitoring ordiagnostics.

FIG. 2C illustrates an example Association 260 of tenant entities andaccess entities in MIM 200. Policy Universe 202 contains Tenant Portion204A and Access Portion 206A. Thus, Tenant Portion 204A and AccessPortion 206A are associated through Policy Universe 202.

Access Portion 206A can contain fabric and infrastructure accesspolicies. Typically, in a policy model, EPGs are coupled with VLANs. Fortraffic to flow, an EPG is deployed on a leaf port with a VLAN in aphysical, VMM, L2 out, L3 out, or Fiber Channel domain, for example.

Access Portion 206A thus contains Domain Profile 236 which can define aphysical, VMM, L2 out, L3 out, or Fiber Channel domain, for example, tobe associated to the EPGs. Domain Profile 236 contains VLAN InstanceProfile 238 (e.g., VLAN pool) and Attacheable Access Entity Profile(AEP) 240, which are associated directly with application EPGs. The AEP240 deploys the associated application EPGs to the ports to which it isattached, and automates the task of assigning VLANs. While a large datacenter can have thousands of active VMs provisioned on hundreds ofVLANs, Fabric 120 can automatically assign VLAN IDs from VLAN pools.This saves time compared with trunking down VLANs in a traditional datacenter.

FIG. 2D illustrates a schematic diagram of example models for a network,such as Network Environment 100. The models can be generated based onspecific configurations and/or network state parameters associated withvarious objects, policies, properties, and elements defined in MIM 200.The models can be implemented for network analysis and assurance, andmay provide a depiction of the network at various stages ofimplementation and levels of the network.

As illustrated, the models can include L_Model 270A (Logical Model),LR_Model 270B (Logical Rendered Model or Logical Runtime Model),Li_Model 272 (Logical Model for i), Ci_Model 274 (Concrete model for i),and/or Hi_Model 276 (Hardware model or TCAM Model for i).

L_Model 270A is the logical representation of various elements in MIM200 as configured in a network (e.g., Network Environment 100), such asobjects, object properties, object relationships, and other elements inMIM 200 as configured in a network. L_Model 270A can be generated byControllers 116 based on configurations entered in Controllers 116 forthe network, and thus represents the logical configuration of thenetwork at Controllers 116. This is the declaration of the “end-state”expression that is desired when the elements of the network entities(e.g., applications, tenants, etc.) are connected and Fabric 120 isprovisioned by Controllers 116. Because L_Model 270A represents theconfigurations entered in Controllers 116, including the objects andrelationships in MIM 200, it can also reflect the “intent” of theadministrator: how the administrator wants the network and networkelements to behave.

L_Model 270A can be a fabric or network-wide logical model. For example,L_Model 270A can account configurations and objects from each ofControllers 116. As previously explained, Network Environment 100 caninclude multiple Controllers 116. In some cases, two or more Controllers116 may include different configurations or logical models for thenetwork. In such cases, L_Model 270A can obtain any of theconfigurations or logical models from Controllers 116 and generate afabric or network wide logical model based on the configurations andlogical models from all Controllers 116. L_Model 270A can thusincorporate configurations or logical models between Controllers 116 toprovide a comprehensive logical model. L_Model 270A can also address oraccount for any dependencies, redundancies, conflicts, etc., that mayresult from the configurations or logical models at the differentControllers 116.

LR_Model 270B is the abstract model expression that Controllers 116(e.g., APICs in ACI) resolve from L_Model 270A. LR_Model 270B canprovide the configuration components that would be delivered to thephysical infrastructure (e.g., Fabric 120) to execute one or morepolicies. For example, LR_Model 270B can be delivered to Leafs 104 inFabric 120 to configure Leafs 104 for communication with attachedEndpoints 122. LR_Model 270B can also incorporate state information tocapture a runtime state of the network (e.g., Fabric 120).

In some cases, LR_Model 270B can provide a representation of L_Model270A that is normalized according to a specific format or expressionthat can be propagated to, and/or understood by, the physicalinfrastructure of Fabric 120 (e.g., Leafs 104, Spines 102, etc.). Forexample, LR_Model 270B can associate the elements in L_Model 270A withspecific identifiers or tags that can be interpreted and/or compiled bythe switches in Fabric 120, such as hardware plane identifiers used asclassifiers.

Li_Model 272 is a switch-level or switch-specific model obtained fromL_Model 270A and/or LR_Model 270B. Li_Model 272 can project L_Model 270Aand/or LR_Model 270B on a specific switch or device i, and thus canconvey how L_Model 270A and/or LR_Model 270B should appear or beimplemented at the specific switch or device i.

For example, Li_Model 272 can project L_Model 270A and/or LR_Model 270Bpertaining to a specific switch i to capture a switch-levelrepresentation of L_Model 270A and/or LR_Model 270B at switch i. Toillustrate, Li_Model 272 L₁ can represent L_Model 270A and/or LR_Model270B projected to, or implemented at, Leaf 1 (104). Thus, Li_Model 272can be generated from L_Model 270A and/or LR_Model 270B for individualdevices (e.g., Leafs 104, Spines 102, etc.) on Fabric 120.

In some cases, Li_Model 272 can be represented using JSON (JavaScriptObject Notation). For example, Li_Model 272 can include JSON objects,such as Rules, Filters, Entries, and Scopes.

Ci_Model 274 is the actual in-state configuration at the individualfabric member i (e.g., switch i). In other words, Ci_Model 274 is aswitch-level or switch-specific model that is based on Li_Model 272. Forexample, Controllers 116 can deliver Li_Model 272 to Leaf 1 (104). Leaf1 (104) can take Li_Model 272, which can be specific to Leaf 1 (104),and render the policies in Li_Model 272 into a concrete model, Ci_Model274, that runs on Leaf 1 (104). Leaf 1 (104) can render Li_Model 272 viathe OS on Leaf 1 (104), for example. Thus, Ci_Model 274 can be analogousto compiled software, as it is the form of Li_Model 272 that the switchOS at Leaf 1 (104) can execute.

In some cases, Li_Model 272 and Ci_Model 274 can have a same or similarformat. For example, Li_Model 272 and Ci_Model 274 can be based on JSONobjects. Having the same or similar format can facilitate objects inLi_Model 272 and Ci_Model 274 to be compared for equivalence orcongruence. Such equivalence or congruence checks can be used fornetwork analysis and assurance, as further described herein.

Hi_Model 276 is also a switch-level or switch-specific model for switchi, but is based on Ci_Model 274 for switch i. Hi_Model 276 is the actualconfiguration (e.g., rules) stored or rendered on the hardware or memory(e.g., TCAM memory) at the individual fabric member i (e.g., switch i).For example, Hi_Model 276 can represent the configurations (e.g., rules)which Leaf 1 (104) stores or renders on the hardware (e.g., TCAM memory)of Leaf 1 (104) based on Ci_Model 274 at Leaf 1 (104). The switch OS atLeaf 1 (104) can render or execute Ci_Model 274, and Leaf 1 (104) canstore or render the configurations from Ci_Model 274 in storage, such asthe memory or TCAM at Leaf 1 (104). The configurations from Hi_Model 276stored or rendered by Leaf 1 (104) represent the configurations thatwill be implemented by Leaf 1 (104) when processing traffic.

While Models 272, 274, 276 are shown as device-specific models, similarmodels can be generated or aggregated for a collection of fabric members(e.g., Leafs 104 and/or Spines 102) in Fabric 120. When combined,device-specific models, such as Model 272, Model 274, and/or Model 276,can provide a representation of Fabric 120 that extends beyond aparticular device. For example, in some cases, Li_Model 272, Ci_Model274, and/or Hi_Model 276 associated with some or all individual fabricmembers (e.g., Leafs 104 and Spines 102) can be combined or aggregatedto generate one or more aggregated models based on the individual fabricmembers.

As referenced herein, the terms H Model, T Model, and TCAM Model can beused interchangeably to refer to a hardware model, such as Hi_Model 276.For example, Ti Model, Hi_Model and TCAMi Model may be usedinterchangeably to refer to Hi_Model 276.

Models 270A, 270B, 272, 274, 276 can provide representations of variousaspects of the network or various configuration stages for MIM 200. Forexample, one or more of Models 270A, 270B, 272, 274, 276 can be used togenerate Underlay Model 278 representing one or more aspects of Fabric120 (e.g., underlay topology, routing, etc.), Overlay Model 280representing one or more aspects of the overlay or logical segment(s) ofNetwork Environment 100 (e.g., COOP, MPBGP, tenants, VRFs, VLANs,VXLANs, virtual applications, VMs, hypervisors, virtual switching,etc.), Tenant Model 282 representing one or more aspects of Tenantportion 204A in MIM 200 (e.g., security, forwarding, service chaining,QoS, VRFs, BDs, Contracts, Filters, EPGs, subnets, etc.), ResourcesModel 284 representing one or more resources in Network Environment 100(e.g., storage, computing, VMs, port channels, physical elements, etc.),etc.

In general, L_Model 270A can be the high-level expression of what existsin the LR_Model 270B, which should be present on the concrete devices asCi_Model 274 and Hi_Model 276 expression. If there is any gap betweenthe models, there may be inconsistent configurations or problems.

FIG. 2E illustrates an equivalency diagram 290 of different models. Inthis example, the L_Model 270A obtained from Controller(s) 116 inNetwork Environment 100 can be compared with the Hi_Model 276 obtainedfrom one or more Leafs 104 in the Fabric 120. This comparison canprovide an equivalency check in order to determine whether the logicalconfiguration of the Network Environment 100 at the Controller(s) 116 isconsistent with, or conflicts with, the rules rendered on the one ormore Leafs 104 (e.g., rules and/or configurations in storage, such asTCAM).

For example, a network operator can define objects and configurationsfor Network Environment 100 from Controller(s) 116. Controller(s) 116can then store the definitions and configurations from the networkoperator and construct a logical model (e.g., L_Model 270A) of theNetwork Environment 100. The Controller(s) 116 can push the definitionsand configurations provided by the network operator and reflected in thelogical model to each of the nodes (e.g., Leafs 104) in the Fabric 120.The nodes in the Fabric 120 can receive such information and rendered orcompile rules on the node's software (e.g., Operating System). The rulesrendered or compiled on the node's software can be constructed into aConstruct Model (e.g., Ci_Model 274). The rules from the Construct Modelcan then be pushed from the node's software to the node's hardware(e.g., TCAM) and stored or rendered as rules on the node's hardware. Therules stored or rendered on the node's hardware can be constructed intoa Hardware Model (e.g., Hi_Model 276) for the node.

The various models (e.g., L_Model 270A and Hi_Model 276) can thusrepresent the rules and configurations at each stage (e.g., intentspecification at Controller(s) 116, rendering or compiling on the node'ssoftware, rendering or storing on the node's hardware, etc.) as thedefinitions and configurations entered by the network operator arepushed through each stage. Accordingly, an equivalency check of variousmodels, such as L_Model 270A and Hi_Model 276, can be used to determinewhether the definitions and configurations have been properly pushed,rendered, and/or stored at each respective stage associated with thevarious models. If the models pass the equivalency check, then thedefinitions and configurations at each stage (e.g., Controller(s) 116,software on the node, hardware on the node, etc.) can be verified asaccurate and consistent. By contrast, if there is an error in theequivalency check, then a misconfiguration can be detected at one ormore specific stages. The equivalency check between various models canalso be used to determine where (e.g., at which stage) the problem ormisconfiguration has occurred. For example, the stage where the problemor misconfiguration occurred can be ascertained based on which model(s)fail the equivalency check.

The L_Model 270A and Hi_Model 276 can store or render the rules,configurations, properties, definitions, etc., in a respective structure292A, 292B. For example, the L_Model 270A can store or render rules,configurations, objects, properties, etc., in a data structure 292A,such as a file or object (e.g., JSON, XML, etc.), and Hi_Model 276 canstore or render rules, configurations, etc., in a storage 292B, such asTCAM memory. The structure 292A, 292B associated with the L_Model 270Aand Hi_Model 276 can influence the format, organization, type, etc., ofthe data (e.g., rules, configurations, properties, definitions, etc.)stored or rendered.

For example, L_Model 270A can store the data as objects and objectproperties 294A, such as EPGs, contracts, filters, tenants, contexts,BDs, network wide parameters, etc. The Hi_Model 276 can store the dataas values and tables 294B, such as value/mask pairs, range expressions,auxiliary tables, etc.

As a result, the data in the L_Model 270A and Hi_Model 276 can benormalized, canonized, diagramed, modeled, re-formatted, flattened,etc., to perform an equivalency between the L_Model 270A and Hi_Model276. For example, the data can be converted using bit vectors, Booleanfunctions, ROBDDs, etc., to perform a mathematical check of equivalencybetween the L_Model 270A and Hi_Model 276.

FIG. 3A illustrates a diagram of an example Assurance Appliance 300 fornetwork assurance. In this example, Assurance Appliance 300 can includek VMs 110 operating in cluster mode. VMs are used in this example forexplanation purposes. However, it should be understood that otherconfigurations are also contemplated herein, such as use of containers,bare metal devices, Endpoints 122, or any other physical or logicalsystems. Moreover, while FIG. 3A illustrates a cluster modeconfiguration, other configurations are also contemplated herein, suchas a single mode configuration (e.g., single VM, container, or server)or a service chain for example.

Assurance Appliance 300 can run on one or more Servers 106, VMs 110,Hypervisors 108, EPs 122, Leafs 104, Controllers 116, or any othersystem or resource. For example, Assurance Appliance 300 can be alogical service or application running on one or more VMs 110 in NetworkEnvironment 100.

The Assurance Appliance 300 can include Data Framework 308, which can bebased on, for example, APACHE APEX and HADOOP. In some cases, assurancechecks can be written as individual operators that reside in DataFramework 308. This enables a natively horizontal scale-out architecturethat can scale to arbitrary number of switches in Fabric 120 (e.g., ACIfabric).

Assurance Appliance 300 can poll Fabric 120 at a configurableperiodicity (e.g., an epoch). The analysis workflow can be setup as aDAG (Directed Acyclic Graph) of Operators 310, where data flows from oneoperator to another and eventually results are generated and persistedto Database 302 for each interval (e.g., each epoch).

The north-tier implements API Server (e.g., APACHE Tomcat and Springframework) 304 and Web Server 306. A graphical user interface (GUI)interacts via the APIs exposed to the customer. These APIs can also beused by the customer to collect data from Assurance Appliance 300 forfurther integration into other tools.

Operators 310 in Data Framework 308 (e.g., APEX/Hadoop) can togethersupport assurance operations. Below are non-limiting examples ofassurance operations that can be performed by Assurance Appliance 300via Operators 310.

Security Policy Adherence

Assurance Appliance 300 can check to make sure the configurations orspecification from L_Model 270A, which may reflect the user's intent forthe network, including for example the security policies andcustomer-configured contracts, are correctly implemented and/or renderedin Li_Model 272, Ci_Model 274, and Hi_Model 276, and thus properlyimplemented and rendered by the fabric members (e.g., Leafs 104), andreport any errors, contract violations, or irregularities found.

Static Policy Analysis

Assurance Appliance 300 can check for issues in the specification of theuser's intent or intents (e.g., identify contradictory or conflictingpolicies in L_Model 270A). Assurance Appliance 300 can identify lintevents based on the intent specification of a network. The lint andpolicy analysis can include semantic and/or syntactic checks of theintent specification(s) of a network.

TCAM Utilization

TCAM is a scarce resource in the fabric (e.g., Fabric 120). However,Assurance Appliance 300 can analyze the TCAM utilization by the networkdata (e.g., Longest Prefix Match (LPM) tables, routing tables, VLANtables, BGP updates, etc.), Contracts, Logical Groups 118 (e.g., EPGs),Tenants, Spines 102, Leafs 104, and other dimensions in NetworkEnvironment 100 and/or objects in MIM 200, to provide a network operatoror user visibility into the utilization of this scarce resource. Thiscan greatly help for planning and other optimization purposes.

Endpoint Checks

Assurance Appliance 300 can validate that the fabric (e.g. fabric 120)has no inconsistencies in the Endpoint information registered (e.g., twoleafs announcing the same endpoint, duplicate subnets, etc.), amongother such checks.

Tenant Routing Checks

Assurance Appliance 300 can validate that BDs, VRFs, subnets (bothinternal and external), VLANs, contracts, filters, applications, EPGs,etc., are correctly programmed.

Infrastructure Routing

Assurance Appliance 300 can validate that infrastructure routing (e.g.,IS-IS protocol) has no convergence issues leading to black holes, loops,flaps, and other problems.

MP-BGP Route Reflection Checks

The network fabric (e.g., Fabric 120) can interface with other externalnetworks and provide connectivity to them via one or more protocols,such as Border Gateway Protocol (BGP), Open Shortest Path First (OSPF),etc. The learned routes are advertised within the network fabric via,for example, MP-BGP. These checks can ensure that a route reflectionservice via, for example, MP-BGP (e.g., from Border Leaf) does not havehealth issues.

Logical Lint and Real-Time Change Analysis

Assurance Appliance 300 can validate rules in the specification of thenetwork (e.g., L_Model 270A) are complete and do not haveinconsistencies or other problems. MOs in the MIM 200 can be checked byAssurance Appliance 300 through syntactic and semantic checks performedon L_Model 270A and/or the associated configurations of the MOs in MIM200. Assurance Appliance 300 can also verify that unnecessary, stale,unused or redundant configurations, such as contracts, are removed.

FIG. 3B illustrates an architectural diagram of an example system 350for network assurance, such as Assurance Appliance 300. In some cases,system 350 can correspond to the DAG of Operators 310 previouslydiscussed with respect to FIG. 3A

In this example, Topology Explorer 312 communicates with Controllers 116(e.g., APIC controllers) in order to discover or otherwise construct acomprehensive topological view of Fabric 120 (e.g., Spines 102, Leafs104, Controllers 116, Endpoints 122, and any other components as well astheir interconnections). While various architectural components arerepresented in a singular, boxed fashion, it is understood that a givenarchitectural component, such as Topology Explorer 312, can correspondto one or more individual Operators 310 and may include one or morenodes or endpoints, such as one or more servers, VMs, containers,applications, service functions (e.g., functions in a service chain orvirtualized network function), etc.

Topology Explorer 312 is configured to discover nodes in Fabric 120,such as Controllers 116, Leafs 104, Spines 102, etc. Topology Explorer312 can additionally detect a majority election performed amongstControllers 116, and determine whether a quorum exists amongstControllers 116. If no quorum or majority exists, Topology Explorer 312can trigger an event and alert a user that a configuration or othererror exists amongst Controllers 116 that is preventing a quorum ormajority from being reached. Topology Explorer 312 can detect Leafs 104and Spines 102 that are part of Fabric 120 and publish theircorresponding out-of-band management network addresses (e.g., IPaddresses) to downstream services. This can be part of the topologicalview that is published to the downstream services at the conclusion ofTopology Explorer's 312 discovery epoch (e.g., 5 minutes, or some otherspecified interval).

In some examples, Topology Explorer 312 can receive as input a list ofControllers 116 (e.g., APIC controllers) that are associated with thenetwork/fabric (e.g., Fabric 120). Topology Explorer 312 can alsoreceive corresponding credentials to login to each controller. TopologyExplorer 312 can retrieve information from each controller using, forexample, REST calls. Topology Explorer 312 can obtain from eachcontroller a list of nodes (e.g., Leafs 104 and Spines 102), and theirassociated properties, that the controller is aware of. TopologyExplorer 312 can obtain node information from Controllers 116 including,without limitation, an IP address, a node identifier, a node name, anode domain, a node URI, a node_dm, a node role, a node version, etc.

Topology Explorer 312 can also determine if Controllers 116 are inquorum, or are sufficiently communicatively coupled amongst themselves.For example, if there are n controllers, a quorum condition might be metwhen (n/2+1) controllers are aware of each other and/or arecommunicatively coupled. Topology Explorer 312 can make thedetermination of a quorum (or identify any failed nodes or controllers)by parsing the data returned from the controllers, and identifyingcommunicative couplings between their constituent nodes. TopologyExplorer 312 can identify the type of each node in the network, e.g.spine, leaf, APIC, etc., and include this information in the topologyinformation generated (e.g., topology map or model).

If no quorum is present, Topology Explorer 312 can trigger an event andalert a user that reconfiguration or suitable attention is required. Ifa quorum is present, Topology Explorer 312 can compile the networktopology information into a JSON object and pass it downstream to otheroperators or services, such as Unified Collector 314.

Unified Collector 314 can receive the topological view or model fromTopology Explorer 312 and use the topology information to collectinformation for network assurance from Fabric 120. Unified Collector 314can poll nodes (e.g., Controllers 116, Leafs 104, Spines 102, etc.) inFabric 120 to collect information from the nodes.

Unified Collector 314 can include one or more collectors (e.g.,collector devices, operators, applications, VMs, etc.) configured tocollect information from Topology Explorer 312 and/or nodes in Fabric120. For example, Unified Collector 314 can include a cluster ofcollectors, and each of the collectors can be assigned to a subset ofnodes within the topological model and/or Fabric 120 in order to collectinformation from their assigned subset of nodes. For performance,Unified Collector 314 can run in a parallel, multi-threaded fashion.

Unified Collector 314 can perform load balancing across individualcollectors in order to streamline the efficiency of the overallcollection process. Load balancing can be optimized by managing thedistribution of subsets of nodes to collectors, for example by randomlyhashing nodes to collectors.

In some cases, Assurance Appliance 300 can run multiple instances ofUnified Collector 314. This can also allow Assurance Appliance 300 todistribute the task of collecting data for each node in the topology(e.g., Fabric 120 including Spines 102, Leafs 104, Controllers 116,etc.) via sharding and/or load balancing, and map collection tasksand/or nodes to a particular instance of Unified Collector 314 with datacollection across nodes being performed in parallel by various instancesof Unified Collector 314. Within a given node, commands and datacollection can be executed serially. Assurance Appliance 300 can controlthe number of threads used by each instance of Unified Collector 314 topoll data from Fabric 120.

Unified Collector 314 can collect models (e.g., L_Model 270A and/orLR_Model 270B) from Controllers 116, switch software configurations andmodels (e.g., Ci_Model 274) from nodes (e.g., Leafs 104 and/or Spines102) in Fabric 120, hardware configurations and models (e.g., Hi_Model276) from nodes (e.g., Leafs 104 and/or Spines 102) in Fabric 120, etc.Unified Collector 314 can collect Ci_Model 274 and Hi_Model 276 fromindividual nodes or fabric members, such as Leafs 104 and Spines 102,and L_Model 270A and/or LR_Model 270B from one or more controllers(e.g., Controllers 116) in Network Environment 100.

Unified Collector 314 can poll the devices that Topology Explorer 312discovers in order to collect data from Fabric 120 (e.g., from theconstituent members of the fabric). Unified Collector 314 can collectthe data using interfaces exposed by Controllers 116 and/or switchsoftware (e.g., switch OS), including, for example, a RepresentationState Transfer (REST) Interface and a Secure Shell (SSH) Interface.

In some cases, Unified Collector 314 collects L_Model 270A, LR_Model270B, and/or Ci_Model 274 via a REST API, and the hardware information(e.g., configurations, tables, fabric card information, rules, routes,etc.) via SSH using utilities provided by the switch software, such asvirtual shell (VSH or VSHELL) for accessing the switch command-lineinterface (CLI) or VSH_LC shell for accessing runtime state of the linecard.

Unified Collector 314 can poll other information from Controllers 116,including, without limitation: topology information, tenantforwarding/routing information, tenant security policies, contracts,interface policies, physical domain or VMM domain information, OOB(out-of-band) management IP's of nodes in the fabric, etc.

Unified Collector 314 can also poll information from nodes (e.g., Leafs104 and Spines 102) in Fabric 120, including without limitation:Ci_Models 274 for VLANs, BDs, and security policies; Link LayerDiscovery Protocol (LLDP) connectivity information of nodes (e.g., Leafs104 and/or Spines 102); endpoint information from EPM/COOP; fabric cardinformation from Spines 102; routing information base (RIB) tables fromnodes in Fabric 120; forwarding information base (FIB) tables from nodesin Fabric 120; security group hardware tables (e.g., TCAM tables) fromnodes in Fabric 120; etc.

In some cases, Unified Collector 314 can obtain runtime state from thenetwork and incorporate runtime state information into L_Model 270Aand/or LR_Model 270B. Unified Collector 314 can also obtain multiplelogical models from Controllers 116 and generate a comprehensive ornetwork-wide logical model (e.g., L_Model 270A and/or LR_Model 270B)based on the logical models. Unified Collector 314 can compare logicalmodels from Controllers 116, resolve dependencies, remove redundancies,etc., and generate a single L_Model 270A and/or LR_Model 270B for theentire network or fabric.

Unified Collector 314 can collect the entire network state acrossControllers 116 and fabric nodes or members (e.g., Leafs 104 and/orSpines 102). For example, Unified Collector 314 can use a REST interfaceand an SSH interface to collect the network state. This informationcollected by Unified Collector 314 can include data relating to the linklayer, VLANs, BDs, VRFs, security policies, etc. The state informationcan be represented in LR_Model 270B, as previously mentioned. UnifiedCollector 314 can then publish the collected information and models toany downstream operators that are interested in or require suchinformation. Unified Collector 314 can publish information as it isreceived, such that data is streamed to the downstream operators.

Data collected by Unified Collector 314 can be compressed and sent todownstream services. In some examples, Unified Collector 314 can collectdata in an online fashion or real-time fashion, and send the datadownstream, as it is collected, for further analysis. In some examples,Unified Collector 314 can collect data in an offline fashion, andcompile the data for later analysis or transmission.

Assurance Appliance 300 can contact Controllers 116, Spines 102, Leafs104, and other nodes to collect various types of data. In somescenarios, Assurance Appliance 300 may experience a failure (e.g.,connectivity problem, hardware or software error, etc.) that prevents itfrom being able to collect data for a period of time. AssuranceAppliance 300 can handle such failures seamlessly, and generate eventsbased on such failures.

Switch Logical Policy Generator 316 can receive L_Model 270A and/orLR_Model 270B from Unified Collector 314 and calculate Li_Model 272 foreach network device i (e.g., switch i) in Fabric 120. For example,Switch Logical Policy Generator 316 can receive L_Model 270A and/orLR_Model 270B and generate Li_Model 272 by projecting a logical modelfor each individual node i (e.g., Spines 102 and/or Leafs 104) in Fabric120. Switch Logical Policy Generator 316 can generate Li_Model 272 foreach switch in Fabric 120, thus creating a switch logical model based onL_Model 270A and/or LR_Model 270B for each switch.

Each Li_Model 272 can represent L_Model 270A and/or LR_Model 270B asprojected or applied at the respective network device i (e.g., switch i)in Fabric 120. In some cases, Li_Model 272 can be normalized orformatted in a manner that is compatible with the respective networkdevice. For example, Li_Model 272 can be formatted in a manner that canbe read or executed by the respective network device. To illustrate,Li_Model 272 can included specific identifiers (e.g., hardware planeidentifiers used by Controllers 116 as classifiers, etc.) or tags (e.g.,policy group tags) that can be interpreted by the respective networkdevice. In some cases, Li_Model 272 can include JSON objects. Forexample, Li_Model 272 can include JSON objects to represent rules,filters, entries, scopes, etc.

The format used for Li_Model 272 can be the same as, or consistent with,the format of Ci_Model 274. For example, both Li_Model 272 and Ci_Model274 may be based on JSON objects. Similar or matching formats can enableLi_Model 272 and Ci_Model 274 to be compared for equivalence orcongruence. Such equivalency checks can aid in network analysis andassurance as further explained herein.

Switch Logical Configuration Generator 316 can also perform changeanalysis and generate lint events or records for problems discovered inL_Model 270A and/or LR_Model 270B. The lint events or records can beused to generate alerts for a user or network operator.

Policy Operator 318 can receive Ci_Model 274 and Hi_Model 276 for eachswitch from Unified Collector 314, and Li_Model 272 for each switch fromSwitch Logical Policy Generator 316, and perform assurance checks andanalysis (e.g., security adherence checks, TCAM utilization analysis,etc.) based on Ci_Model 274, Hi_Model 276, and Li_Model 272. PolicyOperator 318 can perform assurance checks on a switch-by-switch basis bycomparing one or more of the models.

Returning to Unified Collector 314, Unified Collector 314 can also sendL_Model 270A and/or LR_Model 270B to Routing Policy Parser 320, andCi_Model 274 and Hi_Model 276 to Routing Parser 326.

Routing Policy Parser 320 can receive L_Model 270A and/or LR_Model 270Band parse the model(s) for information that may be relevant todownstream operators, such as Endpoint Checker 322 and Tenant RoutingChecker 324. Similarly, Routing Parser 326 can receive Ci_Model 274 andHi_Model 276 and parse each model for information for downstreamoperators, Endpoint Checker 322 and Tenant Routing Checker 324.

After Ci_Model 274, Hi_Model 276, L_Model 270A and/or LR_Model 270B areparsed, Routing Policy Parser 320 and/or Routing Parser 326 can sendcleaned-up protocol buffers (Proto Buffs) to the downstream operators,Endpoint Checker 322 and Tenant Routing Checker 324. Endpoint Checker322 can then generate events related to Endpoint violations, such asduplicate IPs, APIPA, etc., and Tenant Routing Checker 324 can generateevents related to the deployment of BDs, VRFs, subnets, routing tableprefixes, etc.

FIG. 3C illustrates a schematic diagram of an example system for staticpolicy analysis in a network (e.g., Network Environment 100). StaticPolicy Analyzer 360 can perform assurance checks to detect configurationviolations, logical lint events, contradictory or conflicting policies,unused contracts, incomplete configurations, etc. Static Policy Analyzer360 can check the specification of the user's intent or intents inL_Model 270A to determine if any configurations in Controllers 116 areinconsistent with the specification of the user's intent or intents.

Static Policy Analyzer 360 can include one or more of the Operators 310executed or hosted in Assurance Appliance 300. However, in otherconfigurations, Static Policy Analyzer 360 can run one or more operatorsor engines that are separate from Operators 310 and/or AssuranceAppliance 300. For example, Static Policy Analyzer 360 can be a VM, acluster of VMs, or a collection of endpoints in a service functionchain.

Static Policy Analyzer 360 can receive as input L_Model 270A fromLogical Model Collection Process 366 and Rules 368 defined for eachfeature (e.g., object) in L_Model 270A. Rules 368 can be based onobjects, relationships, definitions, configurations, and any otherfeatures in MIM 200. Rules 368 can specify conditions, relationships,parameters, and/or any other information for identifying configurationviolations or issues.

Moreover, Rules 368 can include information for identifying syntacticviolations or issues. For example, Rules 368 can include one or morerules for performing syntactic checks. Syntactic checks can verify thatthe configuration of L_Model 270A is complete, and can help identifyconfigurations or rules that are not being used. Syntactic checks canalso verify that the configurations in the hierarchical MIM 200 arecomplete (have been defined) and identify any configurations that aredefined but not used. To illustrate, Rules 368 can specify that everytenant in L_Model 270A should have a context configured configured;every contract in L_Model 270A should specify a provider EPG and aconsumer EPG; every contract in L_Model 270A should specify a subject,filter, and/or port; etc.

Rules 368 can also include rules for performing semantic checks andidentifying semantic violations or issues. Semantic checks can checkconflicting rules or configurations. For example, Rule1 and Rule2 canhave aliasing issues, Rule1 can be more specific than Rule2 and therebycreate conflicts/issues, etc. Rules 368 can define conditions which mayresult in aliased rules, conflicting rules, etc. To illustrate, Rules368 can specify that an allow policy for a specific communicationbetween two objects can conflict with a deny policy for the samecommunication between two objects if the allow policy has a higherpriority than the deny policy, or a rule for an object renders anotherrule unnecessary.

Static Policy Analyzer 360 can apply Rules 368 to L_Model 270A to checkconfigurations in L_Model 270A and output Configuration Violation Events370 (e.g., alerts, logs, notifications, etc.) based on any issuesdetected. Configuration Violation Events 370 can include semantic orsemantic problems, such as incomplete configurations, conflictingconfigurations, aliased rules, unused configurations, errors, policyviolations, misconfigured objects, incomplete configurations, incorrectcontract scopes, improper object relationships, etc.

In some cases, Static Policy Analyzer 360 can iteratively traverse eachnode in a tree generated based on L_Model 270A and/or MIM 200, and applyRules 368 at each node in the tree to determine if any nodes yield aviolation (e.g., incomplete configuration, improper configuration,unused configuration, etc.). Static Policy Analyzer 360 can outputConfiguration Violation Events 370 when it detects any violations.

The disclosure now turns to FIGS. 4A, 4B, and 4C, which illustrateexample methods. FIG. 4A illustrates an example method for networkassurance, FIG. 4B illustrates an example method for obtaining atopological model of the network, collecting configurations from thenodes in the topological model, and generating a node-specific model,and FIG. 4C illustrates an example method for obtaining a network-widelogical model of the network and generating a node-specific logicalmodel based on the network-wide logical model. The methods are providedby way of example, as there are a variety of ways to carry out themethods. Additionally, while the example methods are illustrated with aparticular order of blocks or steps, those of ordinary skill in the artwill appreciate that FIGS. 4A-4C, and the blocks shown therein, can beexecuted in any order and can include fewer or more blocks thanillustrated.

Each block shown in FIGS. 4A-4C represents one or more steps, processes,methods or routines in the methods. For the sake of clarity andexplanation purposes, the blocks in FIGS. 4A-4C are described withreference to Assurance Appliance 300, Models 270A-B, 272, 274, 276, andNetwork Environment 100, as shown in FIGS. 1A-B, 2D, and 3A.

With reference to FIG. 4A, at step 400, Assurance Appliance 300 cancollect data and obtain models associated with Network Environment 100.The models can include Models 270A-B, 272, 274, 276. The data caninclude fabric data (e.g., topology, switch, interface policies,application policies, EPGs, etc.), network configurations (e.g., BDs,VRFs, L2 Outs, L3 Outs, protocol configurations, etc.), securityconfigurations (e.g., contracts, filters, etc.), service chainingconfigurations, routing configurations, and so forth. Other informationcollected or obtained can include, for example, network data (e.g.,RIB/FIB, VLAN, MAC, ISIS, DB, BGP, OSPF, ARP, VPC, LLDP, MTU, QoS,etc.), rules and tables (e.g., TCAM rules, ECMP tables, etc.), endpointdynamics (e.g., EPM, COOP EP DB, etc.), statistics (e.g., TCAM rulehits, interface counters, bandwidth, etc.).

At step 402, Assurance Appliance 300 can analyze and model the receiveddata and models. For example, Assurance Appliance 300 can perform formalmodeling and analysis, which can involve determining equivalency betweenmodels, including configurations, policies, etc.

At step 404, Assurance Appliance 300 can generate one or more smartevents. Assurance Appliance 300 can generate smart events using deepobject hierarchy for detailed analysis, such as Tenants, switches, VRFs,rules, filters, routes, prefixes, ports, contracts, subjects, etc.

At step 406, Assurance Appliance 300 can visualize the smart events,analysis and/or models. Assurance Appliance 300 can display problems andalerts for analysis and debugging, in a user-friendly GUI.

With reference to FIG. 4B, at step 420, Assurance Appliance 300 canobtain, from Controllers 116 in Network Environment 100, a logical model(e.g., L_Model 270A and/or LR_Model 270B) of the Network Environment100. The logical model can contain a plurality of objects configured forthe Network Environment 100 from a hierarchical management informationtree (MIT), such as MIM 200, associated with the Network Environment 100and represent configurations of the plurality of objects. The pluralityof objects can correspond to manageable objects from the MIT, and theconfigurations can correspond to object properties and/or relationshipsdefined based on the MIT.

At step 422, Assurance Appliance 300 can obtain a topological model ofFabric 120 in Network Environment 100. The topological model canrepresent the topology of the Fabric 120, including Leafs 104, Spines102, Controllers 116, etc. Moreover, the topological model can includenode identifiers, node configuration data, node addressing data, etc.Assurance Appliance 300 can use the topological model to identify thevarious nodes in the Fabric 120 and communicate with any of the nodes ifnecessary.

Based on the topological model, at step 424, Assurance Appliance 300 canpoll nodes (e.g., Leafs 104 and Spines 102) in Fabric 120 for respectiveconfiguration information at the nodes. The respective configurationinformation can include rules, configurations, runtime state, models,policies, etc. Assurance Appliance 300 can specify what to be includedin the respective configuration information.

In some examples, Assurance Appliance 300 can poll the nodes forconfiguration information such as rules at the nodes, configurationsettings at the nodes, runtime state data at the nodes, models at thenodes, etc. In some examples, the models at the nodes can includeconcrete and/or hardware models. Thus, Assurance Appliance 300 can pollthe nodes to obtain a respective concrete model (e.g., Ci_Model 274)and/or hardware model (e.g., Hi_Model 276) from the nodes. AssuranceAppliance 300 can poll the nodes for various types of configurationinformation, such as VLANs, BDs, security policies, LLDP information, IPinformation, prefixes, fabric card information, RIB/FIB tables, securitygroup tables, interface information, etc.

Based on the respective configuration information, at step 426,Assurance Appliance 300 can obtain or generate a respectivenode-specific representation of the logical model. The respectivenode-specific representation of the logical model can project thelogical model to each respective node. For example, the respectivenode-specific representation of the logical model can include arespective Ci_Model 274 and/or Hi_Model 276. The respectivenode-specific representation of the logical model can include theportion of the logical model from the perspective of the specific node,including any configuration information in the logical model pertainingto that specific node. For example, the respective node-specificrepresentation of the logical model can be a node-specific logical modelfor that specific node, which can include those objects and/orconfigurations from the logical model deployed and/or available at thenode.

Referring to FIG. 4C, at step 440 Assurance Appliance 300 can obtain,from Controllers 116, one or more logical models associated with NetworkEnvironment 100. The one or more logical models can include objects andconfigurations stored at Controllers 116 for Network Environment 100.For example, the logical models can be objects and object propertiesdefined at Controllers 116 for Network Environment 100 based on theobjects and object properties available for configuration or definitionbased on a hierarchical MIT (e.g., MIM 200) associated with NetworkEnvironment 100 (e.g., an MIT associated with an ACI network).

Based on the one or more logical models and the configurations, at step442, Assurance Appliance 300 can generate a network-wide logical model(e.g., L_Model 270A) of Network Environment 100. The network-widelogical model can represent a network-wide configuration of objectsassociated with a hierarchical MIT (e.g., MIM 200) associated with theNetwork Environment 100. In some examples, Assurance Appliance 300 cancombine logical models from various Controllers 116 in the NetworkEnvironment 100 to construct a single, network-wide logical model forthe Network Environment 100.

At step 444, Assurance Appliance 300 can generate, based on thenetwork-wide logical model, a rendered (or runtime) logical model (e.g.,LR_Model 270B) of the Network Environment 100. The rendered logicalmodel can include a runtime state of the Network Environment 100. Therendered logical model can be formatted in a manner that can be read,executed, rendered, and/or interpreted by nodes in Fabric 120, such asLeafs 104 and Spines 102. In some cases, the rendered logical model canbe a flat model of the network-wide logical model, and can containobjects and/or identifiers that are understood by the nodes in Fabric120, such as JSON objects, hardware plane identifiers, policy grouptags, key/value pairs, etc. Moreover, the rendered logical model can bea network-wide logical model for the Network Environment 100 formattedfor rendering and/or compilation at the nodes in the Fabric 120, and/orincluding runtime state data collected for the Network Environment 100.

Based on the rendered logical model, at step 446, Assurance Appliance300 can generate, for one or more nodes in the Network Environment 100(e.g., Leafs 104 and/or Spines in Fabric 120), a respectivedevice-specific representation of the network-wide logical model (e.g.,Li_Model 272). The respective device-specific representation can begenerated by rendering, compiling, or propagating the rendered logicalmodel onto a respective node (e.g., Leaf N 104) in order to project thenetwork-wide logical model onto that respective node. In other words,the respective device-specific representation can convey how thenetwork-wide logical model is perceived at the respective node. Forexample, the respective device-specific representation can be aswitch-specific logical model that represents the network-wide logicalmodel as perceived, projected, applicable, etc., to that particularswitch, including any configurations in the network-wide logical modelthat are rendered, compiled, and/or deployed at the particular switch.

With reference to FIGS. 5A-C, the disclosure now turns to a descriptionof example contracts, objects, and configurations defined for an SDNnetwork, such as Network Environment 100. FIG. 5A illustrates aschematic diagram of Graphical Representation 500 for contract andobject configurations in an SDN network (e.g., Network Environment 100).The contract and object configurations can depict communication policiesand object relationships defined in an SDN network via one or morecontracts and objects from a corresponding object model, such as MIM200. The contract scope can define the scope of a contract between twoor more participating entities or EPGs. Example contract scopes caninclude Private Network or Context (e.g., VRF), Tenant, ApplicationProfile, and Global. The Global scope can be applied to any EPGs orentities throughout the fabric.

In the example Graphical Representation 500, the SDN network (e.g.,Network Environment 100) is represented by SDN 502. Here, SDN 502 is theroot node in Graphical Representation 500 and can represent one or moreSDN network frameworks, such as ACI and any other SDN networkframeworks. For illustration purposes, SDN 502 can represent an ACIframework and/or portion implemented in Network Environment 100.

SDN 502 includes one or more child nodes, represented by Tenant Objects504, 506, 508 in this example. It should be noted that while TenantObjects 504, 506, 508 are illustrated as child nodes from SDN 502, otherconfigurations can include other types of objects configured as childnodes from SDN 502, exclude Tenant Objects 504, 506, 508 from GraphicalRepresentation 500, and/or include tenant objects in lower levels of thetaxonomy represented under SDN 502.

Tenant Objects 504, 506, 508 represent particular tenants or tenantcontainers configured in the SDN network. Tenant Objects 504, 506, 508can be provided by a system (e.g., one or more Controllers 116), orconfigured by a user, such as the fabric administrator. In this example,Tenant Object 504 corresponds to Tenant Object A, Tenant Object 506corresponds to Tenant Object B, and Tenant Object 508 corresponds to thetenant “Common”. The common tenant contains policies that govern theoperation of resources and/or contracts accessible to all tenants, suchas firewalls, load balancers, Layer 4 to Layer 7 services, intrusiondetection appliances, and so on. As referenced herein, the prefix “Tn”represents Tenant. For example, “Tn-common” represents the commontenant.

Tenant Objects 504, 506, 508 can also have child nodes representingother objects in SDN 502. For example, Contract 524 can be configuredfor, or applied to, the common tenant, Tenant Object 508. In thisexample, Contract 524 includes Policy 526 which permits traffic on port80 (i.e., HTTP traffic). Tenant Object 504 includes child nodes 510,512, 514, 516. Child nodes 510, 512, 514, 516 include WebEPG Object 510,which depicts an EPG container or attribute for one or more Webapplications associated with Tenant A, Contracts 512, which representsone or more contracts defined for Tenant A, and Context Objects 514,516, which represent Private Networks or Contexts (e.g., VRF instances),such as Context 1 and Context 2, associated with Tenant A.

Tenant Object 506 can include child nodes 518, 520, 522. Child nodes518, 520, 522 include AppEPG 518 which depicts an EPG container orattribute for an application EPG associated with Tenant Object 506(i.e., Tenant B), Contract 520 which represents one or more contractsdefined for Tenant Object 506 (i.e., Tenant B), and Context Object 522which represents Context 1 configured for Tenant Object 506 (i.e.,Tenant B).

Graphical Representation 500 also illustrates an example contract,Contract 528 from Contracts 512, defined for Tenant Object 504 (i.e.,Tenant A). As previously noted, contracts can be used to define orcontrol traffic flow between objects in the SDN, such as communicationsbetween EPGs in the SDN. In this example, Contract 528 definescommunications or traffic between WebEPG Object 510 in Tenant Object 504(i.e., Tenant A) and AppEPG Object 518 in Tenant Object 506 (i.e.,Tenant B). Thus, Contract 528 may permit communications between webservices associated with WebEPG Object 510 in Tenant Object 504 (i.e.,Tenant A) and applications or application services associated withAppEPG Object 518 in Tenant Object 506 (i.e., Tenant B).

Contracts can include various configurable properties, such as a name,priority (e.g., QoS), codepoint (e.g., DSCP codepoint), scope (e.g.,Application Profile, Private Network or Context, Tenant, Global, EPG,etc.), description, etc. Moreover, a contract can include variouselements for defining communication parameters associated with thecontract, such as labels, aliases, filters (e.g., Layer 2 through Layer4 attributes for classifying traffic), subjects (e.g., a group offilters for a particular application or service), actions (e.g., permittraffic, mark traffic, redirect traffic, copy traffic, block traffic,log traffic, etc.), etc.

In some cases, contract properties can define whether an EPG or serviceassociated with a contract is a consumer of the contract or a providerof the contract. For example, EPGs can provide, consume, or both provideand consume contracts. A contract provider can be the provider of aservice, such as a web service, and a contract consumer can be theconsumer of a service provided by someone else, such as the consumer ofthe web service. Contract properties can also be used to define trafficflow direction, such as bi-directional, consumer to provider, providerto consumer, etc.

For example, when a contract is associated with an EPG, that EPG can bedefined as a provider or consumer of the contract. To illustrate, assumeWebEPG Object 510 provides a web service (e.g., HTTP) that needs to beaccessed by AppEPG Object 518. In this case, WebEPG Object 510 can beset as the provider of the contract (and its associated filter) allowingweb traffic, and AppEPG Object 518 can be set as the consumer of thecontract.

FIG. 5B illustrates a representation of Contract 528 (e.g.,Web-Contract) between WebEPG Object 510 associated with Tenant Object504 (i.e., Tenant A) and AppEPG Object 518 associated with Tenant Object506 (i.e., Tenant B). In Contract 528, WebEPG Object 510 is defined as aprovider of Contract 528 and AppEPG Object 518 is defined as a consumerof Contract 528. Contract 528 can be implemented to allow applicationsassociated with WebEPG Object 510 to provide web traffic or services toapplications associated with AppEPG Object 518.

When defining a contract, a scope of the contract can be defined toprovide a level of enforcement for the contract or define anaccessibility of the contract. Example contract scopes can includePrivate Network or Context (e.g., VRF), Application Profile, Tenant,Global, etc. The scope of a contract can define the flow of trafficand/or level of policy enforcement between the various entities, such asEPGs, within the SDN.

FIG. 5C illustrates an example configuration of objects in an SDNnetwork, such as Network Environment 100. The configuration of objectsin this example can include Tenant Objects 562A-C, Context Objects564A-C, BD Objects 566A-B, EPG Objects 568A-E, Contracts, and contractproperties, such as Contract Scope 574, Provider Attributes 570 andConsumer Attributes 572. Such configuration of objects can define theflow of traffic between entities, such as applications, devices, andtenants, as well as security and enforcement policies.

In this example, Tenant Object 562A is defined as Tenant IT Shared,Tenant Object 562B is defined as Tenant Employee, and Tenant Object 562Cis defined as Tenant Finance. Tenant Object 562A (i.e., Tenant ITShared) is set as a Provider 570 of the contract while Tenant Object562B (i.e., Tenant Employee) and Tenant Object 562C (i.e., TenantFinance) are set as Consumers 572 of the contract.

Tenant Object 562A has a Context Object 564A set to VRF Shared, a BDObject 566A set to Shared-BD, and an EPG Object 568A for EPG-SSO. TenantObject 562B has a Context Object 564B set to VRF Shared, a BD Object566B set to VDI-BD, and an EPG Object 568B set to EPG-VDI. Tenant Object562C has a Context 564C set to VRF Internal, does not have acorresponding BD Object configured, and includes EPG Objects 568C-E setto EPG-Web, EPG-APP, and EPG-DB, respectively.

The Contract Scope 574 is set to Context (VRF) between Provider 570 andConsumer(s) 572. As previously illustrated, EPG-SSO associated with EPGObject 568A of Tenant Object 562A, which is set as Provider 570, has thesame context (i.e., Context Object 564A is set to VRF Shared) as EPG-VDIassociated with EPG Object 568B of Tenant Object 562B (i.e., ContextObject 564B is set to VRF Shared), which is set as Consumer 572.However, EPG-Web, EPG-App, and EPG-DB respectively associated with EPGObjects 568C-E of Tenant Object 562C, which are set as Consumers 572,have a different context (i.e., Context Object 564C is set to VRFInternal) than EPG-SSO associated with EPG Object 568A and EPG-VDIassociated with EPG Object 568B. Accordingly, in this example,communications between EPG-SSO associated with EPG Object 568A andEPG-VDI associated with EPG Object 568B will be permitted, as they havea matching context (i.e., VRF Shared defined for their Context Objects564A and 564B). However, communications between EPG-SSO associated withEPG Object 568A and EPG-Web, EPG-App, and EPG-DB, respectivelyassociated with EPG Objects 568C-E, will not be permitted, as theContract Scope 574 between Provider 570 and Consumers 572 is set toContext and the Context Objects 564A and 564C in this example aredifferent.

As illustrated above, the Contract Scope 574 is incorrectly set asbetween EPG-SSO associated with EPG Object 568A and EPG-Web, EPG-App,and EPG-DB, respectively associated with EPG Objects 568C-E, and willnot permit communications between them. This can be fixed by changingthe Contract Scope 574 to GLOBAL or changing the Context Objects 564Aand 564C to match. A contract scope of Global would allow the associatedcontract to be applied to all of the EPGs associated with the contract,namely, EPGs 568A through 568E.

In another example, a network operator creates EPG1 in BD1 (subnet10.1.1.1/16) and VRF1 for Tenant1, EPG2 in BD2 (subnet 10.1.1.1/24) andVRF2 for Tenant2. The network operator also creates a contract C1 with ascope of VRF in Tenant2. The network operator attaches EPG1 as Providerand EPG2 as Consumer of the contract C1. However, due to scoping rules,EPG1 and EPG2 will not be bound even though the customer may think thatthe contract C1 is deployed. Here, the scope should be changed to putthe Provider and Consumer on the same VRF or set the scope of theConsumer to Global.

In yet another example, assume two tenants (e.g., IT and Employee) havetwo separate VRFs, and one tenant is set as Provider and includes aWeb-EPG while the second tenant is set as Consumer and includes anVDI-EPG. The two tenants (IT and Employee) have separate BDs (e.g.,Shared-BD and VDI-BD), but the BDs have the same network addresses,10.0.0.1/24. However, the contract scope has an incorrect setting (e.g.,scopes do not match or the contract does not have a Consumer attached toit). Thus, the contract scope of the Consumer can be fixed by changingit to Global. However, after the contract scope is fixed by changing itto Global, forwarding decisions will still be impacted by the separateBDs having the same network addresses.

Assurance Appliance 300 can perform a policy analysis to identify the IPsubnet conflict, and generate an event, such as “OVERLAPPING SUBNETSACROSS VRFS DUE TO CONTRACT”, to indicate that the IP subnet conflicthas occurred. In order to fix this IP subnet conflict, the IP addresssubnets can be modified so they are not overlapping.

FIG. 6 illustrates an example Application Profile 602 configured toinclude EPG 510, EPG 518, and EPG 604. In this example, EPG 510, EPG518, and EPG 604 include WebEPG, AppEPG, and DBEPG, respectively. Thus,the Application Profile 602 includes WebEPG, AppEPG, and DBEPG.Moreover, EPG 510, EPG 518, and EPG 604 include respective EPs 122,which can communicate between EPGs based on one or more contracts andthe Application Profile 602.

Application Profile 602 can be used to associate EPG 510, EPG 518, EPG604, and the respective EPs 122 in EPG 510, EPG 518, EPG 604, as well asany corresponding filters, contracts, actions, etc. Application Profile602 can also be used to define the scope of a contract. Thus, with thescope of the contract set to Application Profile, a provider attached tothe contract having Application Profile 602 will be able to communicatewith a consumer attached to the contract also having Application Profile602.

The disclosure now turns to FIGS. 7 and 8, which illustrate examplecomputing and network devices, such as switches, routers, loadbalancers, client devices, and so forth.

FIG. 7 illustrates a computing system architecture 700 wherein thecomponents of the system are in electrical communication with each otherusing a connection 705, such as a bus. Exemplary system 700 includes aprocessing unit (CPU or processor) 710 and a system connection 705 thatcouples various system components including the system memory 715, suchas read only memory (ROM) 720 and random access memory (RAM) 725, to theprocessor 710. The system 700 can include a cache of high-speed memoryconnected directly with, in close proximity to, or integrated as part ofthe processor 710. The system 700 can copy data from the memory 715and/or the storage device 730 to the cache 712 for quick access by theprocessor 710. In this way, the cache can provide a performance boostthat avoids processor 710 delays while waiting for data. These and othermodules can control or be configured to control the processor 710 toperform various actions. Other system memory 715 may be available foruse as well. The memory 715 can include multiple different types ofmemory with different performance characteristics. The processor 710 caninclude any general purpose processor and a hardware or softwareservice, such as service 1 732, service 2 734, and service 3 736 storedin storage device 730, configured to control the processor 710 as wellas a special-purpose processor where software instructions areincorporated into the actual processor design. The processor 710 may bea completely self-contained computing system, containing multiple coresor processors, a bus, memory controller, cache, etc. A multi-coreprocessor may be symmetric or asymmetric.

To enable user interaction with the computing device 700, an inputdevice 745 can represent any number of input mechanisms, such as amicrophone for speech, a touch-sensitive screen for gesture or graphicalinput, keyboard, mouse, motion input, speech and so forth. An outputdevice 735 can also be one or more of a number of output mechanismsknown to those of skill in the art. In some instances, multimodalsystems can enable a user to provide multiple types of input tocommunicate with the computing device 700. The communications interface740 can generally govern and manage the user input and system output.There is no restriction on operating on any particular hardwarearrangement and therefore the basic features here may easily besubstituted for improved hardware or firmware arrangements as they aredeveloped.

Storage device 730 is a non-volatile memory and can be a hard disk orother types of computer readable media which can store data that areaccessible by a computer, such as magnetic cassettes, flash memorycards, solid state memory devices, digital versatile disks, cartridges,random access memories (RAMs) 725, read only memory (ROM) 720, andhybrids thereof.

The storage device 730 can include services 732, 734, 736 forcontrolling the processor 710. Other hardware or software modules arecontemplated. The storage device 730 can be connected to the systemconnection 705. In one aspect, a hardware module that performs aparticular function can include the software component stored in acomputer-readable medium in connection with the necessary hardwarecomponents, such as the processor 710, connection 705, output device735, and so forth, to carry out the function.

FIG. 8 illustrates an example network device 800 suitable for performingswitching, routing, load balancing, and other networking operations.Network device 800 includes a central processing unit (CPU) 804,interfaces 802, and a bus 810 (e.g., a PCI bus). When acting under thecontrol of appropriate software or firmware, the CPU 804 is responsiblefor executing packet management, error detection, and/or routingfunctions. The CPU 804 preferably accomplishes all these functions underthe control of software including an operating system and anyappropriate applications software. CPU 804 may include one or moreprocessors 808, such as a processor from the INTEL X86 family ofmicroprocessors. In some cases, processor 808 can be specially designedhardware for controlling the operations of network device 800. In somecases, a memory 806 (e.g., non-volatile RAM, ROM, etc.) also forms partof CPU 804. However, there are many different ways in which memory couldbe coupled to the system.

The interfaces 802 are typically provided as modular interface cards(sometimes referred to as “line cards”). Generally, they control thesending and receiving of data packets over the network and sometimessupport other peripherals used with the network device 800. Among theinterfaces that may be provided are Ethernet interfaces, frame relayinterfaces, cable interfaces, DSL interfaces, token ring interfaces, andthe like. In addition, various very high-speed interfaces may beprovided such as fast token ring interfaces, wireless interfaces,Ethernet interfaces, Gigabit Ethernet interfaces, ATM interfaces, HSSIinterfaces, POS interfaces, FDDI interfaces, WIFI interfaces, 3G/4G/5Gcellular interfaces, CAN BUS, LoRA, and the like. Generally, theseinterfaces may include ports appropriate for communication with theappropriate media. In some cases, they may also include an independentprocessor and, in some instances, volatile RAM. The independentprocessors may control such communications intensive tasks as packetswitching, media control, signal processing, crypto processing, andmanagement. By providing separate processors for the communicationsintensive tasks, these interfaces allow the master microprocessor 604 toefficiently perform routing computations, network diagnostics, securityfunctions, etc.

Although the system shown in FIG. 8 is one specific network device ofthe present invention, it is by no means the only network devicearchitecture on which the present invention can be implemented. Forexample, an architecture having a single processor that handlescommunications as well as routing computations, etc., is often used.Further, other types of interfaces and media could also be used with thenetwork device 800.

Regardless of the network device's configuration, it may employ one ormore memories or memory modules (including memory 806) configured tostore program instructions for the general-purpose network operationsand mechanisms for roaming, route optimization and routing functionsdescribed herein. The program instructions may control the operation ofan operating system and/or one or more applications, for example. Thememory or memories may also be configured to store tables such asmobility binding, registration, and association tables, etc. Memory 806could also hold various software containers and virtualized executionenvironments and data.

The network device 800 can also include an application-specificintegrated circuit (ASIC), which can be configured to perform routingand/or switching operations. The ASIC can communicate with othercomponents in the network device 800 via the bus 810, to exchange dataand signals and coordinate various types of operations by the networkdevice 800, such as routing, switching, and/or data storage operations,for example.

For clarity of explanation, in some instances the present technology maybe presented as including individual functional blocks includingfunctional blocks comprising devices, device components, steps orroutines in a method embodied in software, or combinations of hardwareand software.

In some embodiments the computer-readable storage devices, mediums, andmemories can include a cable or wireless signal containing a bit streamand the like. However, when mentioned, non-transitory computer-readablestorage media expressly exclude media such as energy, carrier signals,electromagnetic waves, and signals per se.

Methods according to the above-described examples can be implementedusing computer-executable instructions that are stored or otherwiseavailable from computer readable media. Such instructions can comprise,for example, instructions and data which cause or otherwise configure ageneral purpose computer, special purpose computer, or special purposeprocessing device to perform a certain function or group of functions.Portions of computer resources used can be accessible over a network.The computer executable instructions may be, for example, binaries,intermediate format instructions such as assembly language, firmware, orsource code. Examples of computer-readable media that may be used tostore instructions, information used, and/or information created duringmethods according to described examples include magnetic or opticaldisks, flash memory, USB devices provided with non-volatile memory,networked storage devices, and so on.

Devices implementing methods according to these disclosures can comprisehardware, firmware and/or software, and can take any of a variety ofform factors. Typical examples of such form factors include laptops,smart phones, small form factor personal computers, personal digitalassistants, rackmount devices, standalone devices, and so on.Functionality described herein also can be embodied in peripherals oradd-in cards. Such functionality can also be implemented on a circuitboard among different chips or different processes executing in a singledevice, by way of further example.

The instructions, media for conveying such instructions, computingresources for executing them, and other structures for supporting suchcomputing resources are means for providing the functions described inthese disclosures.

Although a variety of examples and other information was used to explainaspects within the scope of the appended claims, no limitation of theclaims should be implied based on particular features or arrangements insuch examples, as one of ordinary skill would be able to use theseexamples to derive a wide variety of implementations. Further andalthough some subject matter may have been described in languagespecific to examples of structural features and/or method steps, it isto be understood that the subject matter defined in the appended claimsis not necessarily limited to these described features or acts. Forexample, such functionality can be distributed differently or performedin components other than those identified herein. Rather, the describedfeatures and steps are disclosed as examples of components of systemsand methods within the scope of the appended claims.

Claim language reciting “at least one of” refers to at least one of aset and indicates that one member of the set or multiple members of theset satisfy the claim. For example, claim language reciting “at leastone of A and B” means A, B, or A and B.

What is claimed is:
 1. A method comprising: obtaining, from one or morecontrollers in a software-defined network, a logical model of thesoftware-defined network, the logical model containing a plurality ofobjects configured for the software-defined network from a hierarchicalmanagement information tree associated with the software-defined networkand representing configurations of the plurality of objects, thehierarchical management information tree defining manageable objects andobject properties for the software-defined network, the plurality ofobjects corresponding to at least some of the manageable objects;obtaining a topological model of a fabric associated with thesoftware-defined network; based on the topological model, polling nodesin the fabric for respective configuration information at the nodes; andbased on the respective configuration information, generating arespective node-specific representation of the logical model, therespective node-specific representation projecting the logical model toeach respective node.
 2. The method of claim 1, wherein the respectivenode-specific representation of the logical model comprises anode-specific logical model, the node-specific logical model comprisinga first portion of the plurality of objects and a second portion of theconfigurations of the plurality of objects, the first portion of theplurality of objects and the second portion of the configurationspertaining to the respective node associated with the node-specificlogical model.
 3. The method of claim 2, further comprising: collectingruntime state data associated with the network; and modifying thelogical model based on the runtime state data to yield a logical runtimemodel, wherein the respective node-specific representation of thelogical model comprises a respective node-specific representation of thelogical runtime model.
 4. The method of claim 2, wherein polling thenodes further comprises obtaining, from each respective node, at leastone of a respective concrete model and a respective hardware model,wherein the respective concrete model comprises the node-specificlogical model as rendered by software on the respective node, andwherein the respective hardware model comprises the node-specificlogical model as rendered by a hardware storage device on the respectivenode.
 5. The method of claim 2, wherein the plurality of objectscomprises a first set of the manageable objects, and wherein theconfigurations of the plurality of objects comprise respectiveparameters defined for a second set of the object properties, the secondset of the object properties corresponding to the first set of themanageable objects.
 6. The method of claim 2, wherein the nodes compriseswitches in the fabric, and wherein the plurality of objects comprisesat least one of contracts, tenants, endpoint groups, contexts, subjects,filters, entries.
 7. The method of claim 2, wherein the first portion ofthe plurality of objects and the second portion of the configurationspertaining to the respective node comprise those of the plurality ofobjects and the configurations deployed at the respective node.
 8. Themethod of claim 1, wherein obtaining a logical model comprises obtainingrespective logical models from the one or more controllers andgenerating a network-wide logical model based on the respective logicalmodels.
 9. A system comprising: one or more processors; and at least onecomputer-readable storage medium having stored therein instructionswhich, when executed by the one or more processors, cause the system to:obtain, from one or more controllers in a software-defined network, alogical model of the software-defined network, the logical modelcontaining a plurality of objects configured for the software-definednetwork from a hierarchical management information tree associated withthe software-defined network and representing configurations of theplurality of objects, the hierarchical management information treedefining manageable objects and object properties for thesoftware-defined network, the plurality of objects corresponding to atleast some of the manageable objects; obtain a topological model of afabric associated with the software-defined network; based on thetopological model, poll nodes in the fabric for respective configurationinformation at the nodes; and based on the respective configurationinformation, generate a respective node-specific representation of thelogical model, the respective node-specific representation projectingthe logical model to each respective node.
 10. The system of claim 9,wherein the respective node-specific representation of the logical modelcomprises a node-specific logical model, the node-specific logical modelcomprising a first portion of the plurality of objects and a secondportion of the configurations of the plurality of objects, the firstportion of the plurality of objects and the second portion of theconfigurations pertaining to the respective node associated with thenode-specific logical model.
 11. The system of claim 10, wherein the atleast one computer-readable storage medium stores additionalinstructions which, when executed by the one or more processors, causethe system to: collecting runtime state data associated with thenetwork; and modifying the logical model based on the runtime state datato yield a logical runtime model, wherein the respective node-specificrepresentation of the logical model comprises a respective node-specificrepresentation of the logical runtime model.
 12. The system of claim 10,wherein the first portion of the plurality of objects and the secondportion of the configurations pertaining to the respective node comprisethose of the plurality of objects and the configurations deployed at therespective node.
 13. The system of claim 9, wherein polling the nodesfurther comprises obtaining, from each respective node, at least one ofa respective concrete model and a respective hardware model, wherein therespective concrete model comprises the node-specific logical model asrendered by software on the respective node, and wherein the respectivehardware model comprises the node-specific logical model as rendered bya hardware storage device on the respective node.
 14. The system ofclaim 9, wherein the plurality of objects comprises a first set of themanageable objects, and wherein the configurations of the plurality ofobjects comprise respective parameters defined for a second set of theobject properties, the second set of the object properties correspondingto the first set of the manageable objects.
 15. The system of claim 9,wherein the nodes comprise switches in the fabric, and wherein theplurality of objects comprises at least one of contracts, tenants,endpoint groups, contexts, subjects, filters, entries.
 16. The system ofclaim 9, wherein obtaining a logical model comprises obtainingrespective logical models from the one or more controllers andgenerating a network-wide logical model based on the respective logicalmodels.
 17. A non-transitory computer-readable storage mediumcomprising: instructions stored therein instructions which, whenexecuted by one or more processors, cause the one or more processors to:obtain, from one or more controllers in a software-defined network, alogical model of the software-defined network, the logical modelcontaining a plurality of objects configured for the software-definednetwork from a hierarchical management information tree associated withthe software-defined network and representing configurations of theplurality of objects, the hierarchical management information treedefining manageable objects and object properties for thesoftware-defined network, the plurality of objects being associated withthe manageable objects; obtain a topological model of a fabricassociated with the software-defined network; based on the topologicalmodel, poll nodes in the fabric for respective configuration informationat the nodes; and based on the respective configuration information,generate a respective node-specific representation of the logical model,the respective node-specific representation projecting the logical modelto each respective node.
 18. The non-transitory computer-readablestorage medium of claim 17, wherein the respective node-specificrepresentation of the logical model comprises a node-specific logicalmodel, the node-specific logical model comprising a first portion of theplurality of objects and a second portion of the configurations of theplurality of objects, the first portion of the plurality of objects andthe second portion of the configurations pertaining to the respectivenode associated with the node-specific logical model.
 19. Thenon-transitory computer-readable storage medium of claim 18, storingadditional instructions which, when executed by the one or moreprocessors, cause the one or more processors to: collecting runtimestate data associated with the network; and modifying the logical modelbased on the runtime state data to yield a logical runtime model,wherein the respective node-specific representation of the logical modelcomprises a respective node-specific representation of the logicalruntime model.
 20. The non-transitory computer-readable storage mediumof claim 18, wherein the first portion of the plurality of objects andthe second portion of the configurations pertaining to the respectivenode comprise those of the plurality of objects and the configurationsdeployed at the respective node, wherein the nodes comprise switches inthe fabric, and wherein the plurality of objects comprises at least oneof contracts, tenants, endpoint groups, contexts, subjects, filters,entries.